From f9f30894bee697d60faf46962cfa2dd3ebd3d9a1 Mon Sep 17 00:00:00 2001 From: Irratzo Date: Thu, 3 Jul 2025 18:33:32 +0000 Subject: [PATCH] Update best-of list for version 2025.07.03 --- README.md | 909 ++++++++++++++++---------------- history/2025-07-03_changes.md | 20 + history/2025-07-03_projects.csv | 515 ++++++++++++++++++ latest-changes.md | 20 +- 4 files changed, 991 insertions(+), 473 deletions(-) create mode 100644 history/2025-07-03_changes.md create mode 100644 history/2025-07-03_projects.csv diff --git a/README.md b/README.md index 33f9293..e22b136 100644 --- a/README.md +++ b/README.md @@ -91,7 +91,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for ``` git clone https://github.com/deepmodeling/dpgen ``` -- [PyPi](https://pypi.org/project/dpgen) (📥 580 / month · 📦 2 · ⏱️ 21.02.2025): +- [PyPi](https://pypi.org/project/dpgen) (📥 590 / month · 📦 2 · ⏱️ 21.02.2025): ``` pip install dpgen ``` @@ -102,7 +102,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for
FLARE (🥈20 · ⭐ 330) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP -- [GitHub](https://github.com/mir-group/flare) (👨‍💻 44 · 🔀 70 · 📥 9 · 📦 12 · 📋 220 - 16% open · ⏱️ 24.05.2025): +- [GitHub](https://github.com/mir-group/flare) (👨‍💻 44 · 🔀 72 · 📥 9 · 📦 12 · 📋 220 - 16% open · ⏱️ 24.05.2025): ``` git clone https://github.com/mir-group/flare @@ -115,7 +115,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for ``` git clone https://github.com/zincware/IPSuite ``` -- [PyPi](https://pypi.org/project/ipsuite) (📥 270 / month · 📦 4 · ⏱️ 17.06.2025): +- [PyPi](https://pypi.org/project/ipsuite) (📥 320 / month · 📦 4 · ⏱️ 17.06.2025): ``` pip install ipsuite ``` @@ -128,9 +128,9 @@ _Projects that focus on enabling active learning, iterative learning schemes for git clone https://github.com/deepmodeling/dpgen2 ```
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Bgolearn (🥉13 · ⭐ 93) - [Materials & Design 2024 | NPJ com mat 2024] A Bayesian global optimization package for material design Adaptive.. MIT materials-discovery probabilistic +
Bgolearn (🥉13 · ⭐ 94) - [Materials & Design 2024 | NPJ com mat 2024] A Bayesian global optimization package for material design Adaptive.. MIT materials-discovery probabilistic -- [GitHub](https://github.com/Bin-Cao/Bgolearn) (👨‍💻 3 · 🔀 15 · 📥 54 · 📋 3 - 33% open · ⏱️ 19.06.2025): +- [GitHub](https://github.com/Bin-Cao/Bgolearn) (👨‍💻 3 · 🔀 15 · 📥 57 · 📋 3 - 33% open · ⏱️ 19.06.2025): ``` git clone https://github.com/Bin-Cao/Bgolearn @@ -173,28 +173,28 @@ _Projects that collect atomistic ML resources or foster communication within com
Best-of Machine Learning with Python (🥇22 · ⭐ 21K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python -- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 54 · 🔀 2.8K · 📋 61 - 44% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 54 · 🔀 2.8K · 📋 61 - 44% open · ⏱️ 03.07.2025): ``` git clone https://github.com/ml-tooling/best-of-ml-python ```
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OpenML (🥇19 · ⭐ 700) - Open Machine Learning. BSD-3 datasets +
OpenML (🥇20 · ⭐ 700) - Open Machine Learning. BSD-3 datasets -- [GitHub](https://github.com/openml/OpenML) (👨‍💻 35 · 🔀 95 · 📋 930 - 39% open · ⏱️ 20.06.2025): +- [GitHub](https://github.com/openml/OpenML) (👨‍💻 35 · 🔀 95 · 📋 930 - 39% open · ⏱️ 28.06.2025): ``` git clone https://github.com/openml/OpenML ```
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MatBench Discovery (🥇19 · ⭐ 160 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository +
MatBench Discovery (🥇20 · ⭐ 160) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository -- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 20 · 🔀 39 · 📦 4 · 📋 64 - 6% open · ⏱️ 23.06.2025): +- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 21 · 🔀 40 · 📦 4 · 📋 64 - 6% open · ⏱️ 02.07.2025): ``` git clone https://github.com/janosh/matbench-discovery ``` -- [PyPi](https://pypi.org/project/matbench-discovery) (📥 1.1K / month · ⏱️ 11.09.2024): +- [PyPi](https://pypi.org/project/matbench-discovery) (📥 1K / month · ⏱️ 11.09.2024): ``` pip install matbench-discovery ``` @@ -207,27 +207,27 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/naganandy/graph-based-deep-learning-literature ```
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Garden (🥈18 · ⭐ 31) - FAIR AI/ML Model Publishing Framework. MIT model-repository +
Garden (🥈18 · ⭐ 33) - FAIR AI/ML Model Publishing Framework. MIT model-repository - [GitHub](https://github.com/Garden-AI/garden) (👨‍💻 13 · 🔀 4 · 📦 6 · 📋 340 - 2% open · ⏱️ 16.06.2025): ``` git clone https://github.com/Garden-AI/garden ``` -- [PyPi](https://pypi.org/project/garden-ai) (📥 480 / month · ⏱️ 16.06.2025): +- [PyPi](https://pypi.org/project/garden-ai) (📥 580 / month · ⏱️ 16.06.2025): ``` pip install garden-ai ```
AI for Science Resources (🥈14 · ⭐ 650) - List of resources for AI4Science research, including learning resources. GPL-3.0 license -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 32 · 🔀 75 · 📋 27 - 11% open · ⏱️ 15.06.2025): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 33 · 🔀 76 · 📋 27 - 11% open · ⏱️ 01.07.2025): ``` git clone https://github.com/divelab/AIRS ```
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GT4SD - Generative Toolkit for Scientific Discovery (🥈14 · ⭐ 350) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository +
GT4SD - Generative Toolkit for Scientific Discovery (🥈14 · ⭐ 360) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository - [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 75 · 📋 120 - 11% open · ⏱️ 19.02.2025): @@ -251,7 +251,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/google-deepmind/materials_discovery ```
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Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 140) - A collection of Nerual Network Models for chemistry. MIT rep-learn +
Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 150) - A collection of Nerual Network Models for chemistry. MIT rep-learn - [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 20 · 📋 2 - 50% open · ⏱️ 23.06.2025): @@ -267,9 +267,9 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models ```
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Awesome Materials & Chemistry Datasets (🥈10 · ⭐ 170 · 🐣) - A curated list of the most useful datasets in materials science and chemistry for training machine learning and AI.. MIT datasets experimental-data literature-data proprietary +
Awesome Materials & Chemistry Datasets (🥈10 · ⭐ 180 · 🐣) - A curated list of the most useful datasets in materials science and chemistry for training machine learning and AI.. MIT datasets experimental-data literature-data proprietary -- [GitHub](https://github.com/blaiszik/awesome-matchem-datasets) (👨‍💻 7 · 🔀 19 · 📋 7 - 71% open · ⏱️ 25.06.2025): +- [GitHub](https://github.com/blaiszik/awesome-matchem-datasets) (👨‍💻 8 · 🔀 22 · 📋 7 - 71% open · ⏱️ 27.06.2025): ``` git clone https://github.com/blaiszik/awesome-matchem-datasets @@ -277,7 +277,7 @@ _Projects that collect atomistic ML resources or foster communication within com
DeepModeling Projects (🥈10 · ⭐ 7) - DeepModeling projects. CC-BY-4.0 -- [GitHub](https://github.com/deepmodeling/deepmodeling-projects) (👨‍💻 4 · 🔀 2 · ⏱️ 20.06.2025): +- [GitHub](https://github.com/deepmodeling/deepmodeling-projects) (👨‍💻 4 · 🔀 2 · ⏱️ 27.06.2025): ``` git clone https://github.com/deepmodeling/deepmodeling-projects @@ -381,7 +381,7 @@ _Datasets, databases and trained models for atomistic ML._ 🔗 HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP -🔗 JARVIS-Leaderboard ( ⭐ 68) - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational +🔗 JARVIS-Leaderboard ( ⭐ 69) - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational 🔗 Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API. @@ -409,31 +409,31 @@ _Datasets, databases and trained models for atomistic ML._ 🔗 ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules -
FAIR Chemistry datasets (🥇29 · ⭐ 1.5K) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +
FAIR Chemistry datasets (🥇30 · ⭐ 1.6K · 📈) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis -- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 53 · 🔀 330 · 📋 390 - 6% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 54 · 🔀 350 · 📋 400 - 4% open · ⏱️ 02.07.2025): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 100K / month · 📦 10 · ⏱️ 03.06.2025): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 65K / month · 📦 12 · ⏱️ 01.07.2025): ``` pip install fairchem-core ```
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Meta Open Materials 2024 (OMat24) Dataset (🥇28 · ⭐ 1.5K) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0 +
Meta Open Materials 2024 (OMat24) Dataset (🥇29 · ⭐ 1.6K) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0 -- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 53 · 🔀 330 · 📋 390 - 6% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 54 · 🔀 350 · 📋 400 - 4% open · ⏱️ 02.07.2025): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 100K / month · 📦 10 · ⏱️ 03.06.2025): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 65K / month · 📦 12 · ⏱️ 01.07.2025): ``` pip install fairchem-core ```
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OPTIMADE Python tools (🥇24 · ⭐ 77) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT +
OPTIMADE Python tools (🥇24 · ⭐ 76) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT - [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 31 · 🔀 46 · 📋 470 - 21% open · ⏱️ 10.06.2025): @@ -444,7 +444,7 @@ _Datasets, databases and trained models for atomistic ML._ ``` pip install optimade ``` -- [Conda](https://anaconda.org/conda-forge/optimade) (📥 130K · ⏱️ 22.04.2025): +- [Conda](https://anaconda.org/conda-forge/optimade) (📥 140K · ⏱️ 22.04.2025): ``` conda install -c conda-forge optimade ``` @@ -456,7 +456,7 @@ _Datasets, databases and trained models for atomistic ML._ ``` git clone https://github.com/materialsproject/MPContribs ``` -- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 2.2K / month · 📦 3 · ⏱️ 28.02.2025): +- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 3K / month · 📦 3 · ⏱️ 28.02.2025): ``` pip install mpcontribs-client ``` @@ -469,33 +469,33 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/Materials-Consortia/OPTIMADE ```
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load-atoms (🥈16 · ⭐ 45) - download and manipulate atomistic datasets. MIT data-structures +
load-atoms (🥈16 · ⭐ 45 · 💤) - download and manipulate atomistic datasets. MIT data-structures - [GitHub](https://github.com/jla-gardner/load-atoms) (👨‍💻 4 · 🔀 4 · 📦 8 · 📋 32 - 6% open · ⏱️ 16.12.2024): ``` git clone https://github.com/jla-gardner/load-atoms ``` -- [PyPi](https://pypi.org/project/load-atoms) (📥 1.9K / month · 📦 2 · ⏱️ 13.12.2024): +- [PyPi](https://pypi.org/project/load-atoms) (📥 2K / month · 📦 2 · ⏱️ 13.12.2024): ``` pip install load-atoms ```
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MatPES (🥈15 · ⭐ 37) - A foundational potential energy dataset for materials. BSD-3 UIP ML-IAP +
MatPES (🥈15 · ⭐ 38) - A foundational potential energy dataset for materials. BSD-3 UIP ML-IAP - [GitHub](https://github.com/materialsvirtuallab/matpes) (👨‍💻 3 · 🔀 4 · ⏱️ 02.06.2025): ``` git clone https://github.com/materialsvirtuallab/matpes ``` -- [PyPi](https://pypi.org/project/matpes) (📥 290 / month · ⏱️ 10.03.2025): +- [PyPi](https://pypi.org/project/matpes) (📥 250 / month · ⏱️ 10.03.2025): ``` pip install matpes ```
QH9 (🥈14 · ⭐ 650) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 32 · 🔀 75 · 📋 27 - 11% open · ⏱️ 15.06.2025): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 33 · 🔀 76 · 📋 27 - 11% open · ⏱️ 01.07.2025): ``` git clone https://github.com/divelab/AIRS @@ -508,7 +508,7 @@ _Datasets, databases and trained models for atomistic ML._ ``` git clone https://github.com/valence-labs/openQDC ``` -- [PyPi](https://pypi.org/project/openqdc) (📥 94 / month · ⏱️ 09.08.2024): +- [PyPi](https://pypi.org/project/openqdc) (📥 160 / month · ⏱️ 09.08.2024): ``` pip install openqdc ``` @@ -548,26 +548,26 @@ _Datasets, databases and trained models for atomistic ML._ ``` git clone https://github.com/mpds-io/mpds-api ``` -- [PyPi](https://pypi.org/project/mpds_client) (📥 210 / month · ⏱️ 14.09.2020): +- [PyPi](https://pypi.org/project/mpds_client) (📥 240 / month · ⏱️ 14.09.2020): ``` pip install mpds_client ```
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OBELiX (🥉10 · ⭐ 23 · 🐣) - A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State.. CC-BY-4.0 experimental-data transport-phenomena +
OBELiX (🥉10 · ⭐ 24 · 🐣) - A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State.. CC-BY-4.0 experimental-data transport-phenomena - [GitHub](https://github.com/NRC-Mila/OBELiX) (👨‍💻 5 · 🔀 4 · 📋 2 - 50% open · ⏱️ 16.05.2025): ``` git clone https://github.com/NRC-Mila/OBELiX ``` -- [PyPi](https://pypi.org/project/obelix-data) (📥 60 / month · ⏱️ 16.05.2025): +- [PyPi](https://pypi.org/project/obelix-data) (📥 63 / month · ⏱️ 16.05.2025): ``` pip install obelix-data ```
AIS Square (🥉9 · ⭐ 13) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. LGPL-3.0 community-resource model-repository -- [GitHub](https://github.com/deepmodeling/AIS-Square) (👨‍💻 8 · 🔀 8 · 📋 6 - 83% open · ⏱️ 19.06.2025): +- [GitHub](https://github.com/deepmodeling/AIS-Square) (👨‍💻 8 · 🔀 8 · 📋 6 - 83% open · ⏱️ 30.06.2025): ``` git clone https://github.com/deepmodeling/AIS-Square @@ -581,7 +581,7 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/Ramprasad-Group/polyVERSE ```
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GDB-9-Ex9 and ORNL_AISD-Ex (🥉5 · ⭐ 8 · 📉) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed +
GDB-9-Ex9 and ORNL_AISD-Ex (🥉5 · ⭐ 8) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed - [GitHub](https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python) (👨‍💻 7 · 🔀 6 · ⏱️ 12.03.2025): @@ -603,10 +603,10 @@ _Datasets, databases and trained models for atomistic ML._ - The Perovskite Database Project (🥉5 · ⭐ 65 · 💀) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource - 3DSC Database (🥉4 · ⭐ 20 · 💤) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom superconductors materials-discovery - paper-data-redundancy (🥉4 · ⭐ 11 · 💤) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper -- Visual Graph Datasets (🥉4 · ⭐ 4) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn - OPTIMADE providers dashboard (🥉4 · ⭐ 2) - A dashboard of known providers. Unlicensed - linear-regression-benchmarks (🥉4 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper -- nep-data (🥉2 · ⭐ 17 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena +- Visual Graph Datasets (🥉3 · ⭐ 4) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn +- nep-data (🥉2 · ⭐ 18 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena - tmQM_wB97MV Dataset (🥉1 · ⭐ 7 · 💀) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn

@@ -624,7 +624,7 @@ _Projects that focus on providing data structures used in atomistic machine lear ``` git clone https://github.com/deepmodeling/dpdata ``` -- [PyPi](https://pypi.org/project/dpdata) (📥 26K / month · 📦 40 · ⏱️ 20.03.2025): +- [PyPi](https://pypi.org/project/dpdata) (📥 27K / month · 📦 40 · ⏱️ 20.03.2025): ``` pip install dpdata ``` @@ -635,12 +635,12 @@ _Projects that focus on providing data structures used in atomistic machine lear
Metatensor (🥇23 · ⭐ 77) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 ML-IAP MD Rust C-lang C++ Python -- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 30 · 🔀 22 · 📥 46K · 📦 14 · 📋 250 - 27% open · ⏱️ 19.06.2025): +- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 30 · 🔀 22 · 📥 46K · 📦 14 · 📋 250 - 27% open · ⏱️ 03.07.2025): ``` git clone https://github.com/metatensor/metatensor ``` -- [PyPi](https://pypi.org/project/metatensor) (📥 920 / month · ⏱️ 26.01.2024): +- [PyPi](https://pypi.org/project/metatensor) (📥 1.1K / month · ⏱️ 26.01.2024): ``` pip install metatensor ``` @@ -652,7 +652,7 @@ _Projects that focus on providing data structures used in atomistic machine lear ``` git clone https://github.com/materialsproject/pyrho ``` -- [PyPi](https://pypi.org/project/mp-pyrho) (📥 12K / month · 📦 5 · ⏱️ 22.10.2024): +- [PyPi](https://pypi.org/project/mp-pyrho) (📥 14K / month · 📦 5 · ⏱️ 22.10.2024): ``` pip install mp-pyrho ``` @@ -677,9 +677,9 @@ _Projects and models that focus on quantities of DFT, such as density functional 🔗 M-OFDFT - Overcoming the Barrier of Orbital-Free Density Functional Theory in Molecular Systems Using Deep Learning.. transformer single-paper -
JAX-DFT (🥇26 · ⭐ 36K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 +
JAX-DFT (🥇25 · ⭐ 36K · 📉) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 -- [GitHub](https://github.com/google-research/google-research) (👨‍💻 840 · 🔀 8.1K · 📋 2K - 82% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/google-research/google-research) (👨‍💻 840 · 🔀 8.1K · 📋 2K - 82% open · ⏱️ 30.06.2025): ``` git clone https://github.com/google-research/google-research @@ -695,13 +695,13 @@ _Projects and models that focus on quantities of DFT, such as density functional
QHNet (🥇14 · ⭐ 650) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 32 · 🔀 75 · 📋 27 - 11% open · ⏱️ 15.06.2025): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 33 · 🔀 76 · 📋 27 - 11% open · ⏱️ 01.07.2025): ``` git clone https://github.com/divelab/AIRS ```
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SALTED (🥈13 · ⭐ 38) - Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response). GPL-3.0 +
SALTED (🥈13 · ⭐ 39) - Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response). GPL-3.0 - [GitHub](https://github.com/andreagrisafi/SALTED) (👨‍💻 24 · 🔀 5 · 📋 8 - 25% open · ⏱️ 04.06.2025): @@ -711,20 +711,12 @@ _Projects and models that focus on quantities of DFT, such as density functional
DeepH-pack (🥈12 · ⭐ 280 · 💤) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0 Julia -- [GitHub](https://github.com/mzjb/DeepH-pack) (👨‍💻 8 · 🔀 49 · 📋 65 - 36% open · ⏱️ 07.10.2024): +- [GitHub](https://github.com/mzjb/DeepH-pack) (👨‍💻 8 · 🔀 50 · 📋 65 - 36% open · ⏱️ 07.10.2024): ``` git clone https://github.com/mzjb/DeepH-pack ```
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DeePKS-kit (🥈9 · ⭐ 110) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0 ml-functional - -- [GitHub](https://github.com/deepmodeling/deepks-kit) (👨‍💻 7 · 🔀 36 · 📋 30 - 43% open · ⏱️ 28.04.2025): - - ``` - git clone https://github.com/deepmodeling/deepks-kit - ``` -
Q-stack (🥈9 · ⭐ 18) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool - [GitHub](https://github.com/lcmd-epfl/Q-stack) (👨‍💻 7 · 🔀 5 · 📋 34 - 29% open · ⏱️ 11.06.2025): @@ -741,6 +733,14 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/deepmodeling/dftio ```
+
DeePKS-kit (🥈8 · ⭐ 110) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0 ml-functional + +- [GitHub](https://github.com/deepmodeling/deepks-kit) (👨‍💻 7 · 🔀 36 · 📋 30 - 43% open · ⏱️ 28.04.2025): + + ``` + git clone https://github.com/deepmodeling/deepks-kit + ``` +
HamGNN (🥈8 · ⭐ 100) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang - [GitHub](https://github.com/QuantumLab-ZY/HamGNN) (👨‍💻 2 · 🔀 20 · 📋 52 - 84% open · ⏱️ 09.06.2025): @@ -749,29 +749,29 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/QuantumLab-ZY/HamGNN ```
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CiderPress (🥈8 · ⭐ 12 · 📉) - A high-performance software package for training and evaluating machine-learned XC functionals using the CIDER.. GPL-3.0 ml-functional C-lang +
CiderPress (🥈8 · ⭐ 12) - A high-performance software package for training and evaluating machine-learned XC functionals using the CIDER.. GPL-3.0 ml-functional C-lang - [GitHub](https://github.com/mir-group/CiderPress) (👨‍💻 2 · 🔀 2 · ⏱️ 09.04.2025): ``` git clone https://github.com/mir-group/CiderPress ``` -- [PyPi](https://pypi.org/project/ciderpress) (📥 46 / month · ⏱️ 13.03.2025): +- [PyPi](https://pypi.org/project/ciderpress) (📥 41 / month · ⏱️ 13.03.2025): ``` pip install ciderpress ```
ChargE3Net (🥉7 · ⭐ 59) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn -- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 3 · 🔀 16 · 📋 10 - 40% open · ⏱️ 21.02.2025): +- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 3 · 🔀 16 · 📋 12 - 41% open · ⏱️ 21.02.2025): ``` git clone https://github.com/AIforGreatGood/charge3net ```
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scdp (scalable charge density prediction) (🥉7 · ⭐ 35) - [NeurIPS 2024] source code for A Recipe for Charge Density Prediction. MIT rep-learn single-paper +
scdp (scalable charge density prediction) (🥉7 · ⭐ 35 · 💤) - [NeurIPS 2024] source code for A Recipe for Charge Density Prediction. MIT rep-learn single-paper -- [GitHub](https://github.com/kyonofx/scdp) (🔀 10 · 📋 5 - 20% open · ⏱️ 17.12.2024): +- [GitHub](https://github.com/kyonofx/scdp) (🔀 12 · 📋 5 - 20% open · ⏱️ 17.12.2024): ``` git clone https://github.com/kyonofx/scdp @@ -796,10 +796,10 @@ _Projects and models that focus on quantities of DFT, such as density functional - rho_learn (🥉5 · ⭐ 4 · 💀) - A proof-of-concept workflow for torch-based electron density learning. MIT ML-DFT rep-eng - rholearn (🥉5 · ⭐ 3) - Learning and predicting electronic densities decomposed on a basis and global electronic densities of states at DFT.. MIT ML-DFT rep-eng density-of-states - DeepCDP (🥉4 · ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed -- MALADA (🥉4 · ⭐ 1) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3 - gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper - APET (🥉3 · ⭐ 5 · 💀) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer - CSNN (🥉3 · ⭐ 2 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3 +- MALADA (🥉3 · ⭐ 1) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3 - ofdft_nflows (🥉2 · ⭐ 10 · 💤) - Nomalizing flows for orbita-free DFT. Unlicensed generative - A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed rep-learn single-paper - MLDensity (🥉1 · ⭐ 5 · 💀) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. Unlicensed @@ -819,31 +819,31 @@ _Tutorials, guides, cookbooks, recipes, etc._ 🔗 Quantum Chemistry in the Age of Machine Learning - Book, 2022. -
AI4Chemistry course (🥇12 · ⭐ 190) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry +
AI4Chemistry course (🥇11 · ⭐ 190) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry -- [GitHub](https://github.com/schwallergroup/ai4chem_course) (👨‍💻 7 · 🔀 47 · 📋 4 - 25% open · ⏱️ 30.04.2025): +- [GitHub](https://github.com/schwallergroup/ai4chem_course) (👨‍💻 7 · 🔀 48 · 📋 4 - 25% open · ⏱️ 30.04.2025): ``` git clone https://github.com/schwallergroup/ai4chem_course ```
-
jarvis-tools-notebooks (🥇11 · ⭐ 87) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST +
jarvis-tools-notebooks (🥇11 · ⭐ 88) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST -- [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨‍💻 6 · 🔀 34 · ⏱️ 23.06.2025): +- [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨‍💻 6 · 🔀 35 · ⏱️ 23.06.2025): ``` git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks ```
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COSMO Software Cookbook (🥇11 · ⭐ 23) - A collection of simulation recipes for the atomic-scale modeling of materials and molecules. BSD-3 +
COSMO Software Cookbook (🥇11 · ⭐ 24) - A collection of simulation recipes for the atomic-scale modeling of materials and molecules. BSD-3 -- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 15 · 🔀 4 · 📋 20 - 20% open · ⏱️ 24.06.2025): +- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 15 · 🔀 4 · 📋 20 - 20% open · ⏱️ 02.07.2025): ``` git clone https://github.com/lab-cosmo/software-cookbook ```
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iam-notebooks (🥈10 · ⭐ 28) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2 +
iam-notebooks (🥈9 · ⭐ 28) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2 - [GitHub](https://github.com/ceriottm/iam-notebooks) (👨‍💻 6 · 🔀 5 · ⏱️ 07.01.2025): @@ -851,7 +851,7 @@ _Tutorials, guides, cookbooks, recipes, etc._ git clone https://github.com/ceriottm/iam-notebooks ```
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DSECOP (🥈9 · ⭐ 49) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0 +
DSECOP (🥈8 · ⭐ 49) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0 - [GitHub](https://github.com/GDS-Education-Community-of-Practice/DSECOP) (👨‍💻 14 · 🔀 26 · 📋 8 - 12% open · ⏱️ 29.04.2025): @@ -859,31 +859,31 @@ _Tutorials, guides, cookbooks, recipes, etc._ git clone https://github.com/GDS-Education-Community-of-Practice/DSECOP ```
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DeepModeling Tutorials (🥉7 · ⭐ 15) - Tutorials for DeepModeling projects. Unlicensed +
MLforMaterials (🥉6 · ⭐ 85) - Online resource for a practical course in machine learning for materials research at Imperial College London.. MIT community-resource general-ml rep-eng materials-discovery -- [GitHub](https://github.com/deepmodeling/tutorials) (👨‍💻 11 · 🔀 23 · ⏱️ 03.04.2025): +- [GitHub](https://github.com/aronwalsh/MLforMaterials) (👨‍💻 2 · 🔀 13 · ⏱️ 17.02.2025): ``` - git clone https://github.com/deepmodeling/tutorials + git clone https://github.com/aronwalsh/MLforMaterials ```
-
MLforMaterials (🥉6 · ⭐ 84) - Online resource for a practical course in machine learning for materials research at Imperial College London.. MIT community-resource general-ml rep-eng materials-discovery +
MACE-tutorials (🥉6 · ⭐ 47 · 💤) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD -- [GitHub](https://github.com/aronwalsh/MLforMaterials) (👨‍💻 2 · 🔀 12 · ⏱️ 17.02.2025): +- [GitHub](https://github.com/ilyes319/mace-tutorials) (👨‍💻 2 · 🔀 12 · ⏱️ 16.07.2024): ``` - git clone https://github.com/aronwalsh/MLforMaterials + git clone https://github.com/ilyes319/mace-tutorials ```
-
MACE-tutorials (🥉6 · ⭐ 47 · 💤) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD +
DeepModeling Tutorials (🥉6 · ⭐ 15) - Tutorials for DeepModeling projects. Unlicensed -- [GitHub](https://github.com/ilyes319/mace-tutorials) (👨‍💻 2 · 🔀 12 · ⏱️ 16.07.2024): +- [GitHub](https://github.com/deepmodeling/tutorials) (👨‍💻 11 · 🔀 23 · ⏱️ 03.04.2025): ``` - git clone https://github.com/ilyes319/mace-tutorials + git clone https://github.com/deepmodeling/tutorials ```
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DSM-CORE (🥉6 · ⭐ 15) - Data Science for Materials - Collection of Open Educational Resources. Unlicensed +
DSM-CORE (🥉5 · ⭐ 16) - Data Science for Materials - Collection of Open Educational Resources. Unlicensed - [GitHub](https://github.com/MatSciEdu/DSM-CORE) (👨‍💻 5 · 🔀 7 · 📋 2 - 50% open · ⏱️ 18.06.2025): @@ -893,8 +893,8 @@ _Tutorials, guides, cookbooks, recipes, etc._
Show 20 hidden projects... +- Deep Learning for Molecules and Materials Book (🥇12 · ⭐ 660 · 💀) - Deep learning for molecules and materials book. Custom - DeepLearningLifeSciences (🥇12 · ⭐ 370 · 💀) - Example code from the book Deep Learning for the Life Sciences. MIT -- Deep Learning for Molecules and Materials Book (🥇11 · ⭐ 650 · 💀) - Deep learning for molecules and materials book. Custom - Geometric GNN Dojo (🥇11 · ⭐ 500 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn - Introduction to AI-driven Science on Supercomputers: A Student Training Series (🥇11 · ⭐ 220 · 💤) - Unlicensed general-ml rep-learn language-models - OPTIMADE Tutorial Exercises (🥈9 · ⭐ 15 · 💀) - Tutorial exercises for the OPTIMADE API. MIT datasets @@ -929,7 +929,7 @@ _Projects that focus on explainability and model interpretability in atomistic M ``` git clone https://github.com/ur-whitelab/exmol ``` -- [PyPi](https://pypi.org/project/exmol) (📥 4.2K / month · 📦 3 · ⏱️ 08.05.2025): +- [PyPi](https://pypi.org/project/exmol) (📥 4.9K / month · 📦 3 · ⏱️ 08.05.2025): ``` pip install exmol ``` @@ -950,12 +950,12 @@ _Projects and models that focus on quantities of electronic structure methods, w
DeePTB (🥇16 · ⭐ 79) - DeePTB: A deep learning package for tight-binding Hamiltonian with ab initio accuracy. LGPL-3.0 ML-DFT -- [GitHub](https://github.com/deepmodeling/DeePTB) (👨‍💻 11 · 🔀 20 · 📦 4 · 📋 52 - 36% open · ⏱️ 09.06.2025): +- [GitHub](https://github.com/deepmodeling/DeePTB) (👨‍💻 11 · 🔀 20 · 📦 4 · 📋 52 - 36% open · ⏱️ 30.06.2025): ``` git clone https://github.com/deepmodeling/DeePTB ``` -- [PyPi](https://pypi.org/project/dptb) (📥 88 / month · 📦 2 · ⏱️ 07.05.2025): +- [PyPi](https://pypi.org/project/dptb) (📥 130 / month · 📦 2 · ⏱️ 07.05.2025): ``` pip install dptb ``` @@ -976,14 +976,14 @@ _Projects and models that focus on quantities of electronic structure methods, w _General tools for atomistic machine learning._ -
RDKit (🥇38 · ⭐ 3K) - BSD-3 C++ cheminformatics +
RDKit (🥇37 · ⭐ 3K · 📉) - BSD-3 C++ cheminformatics -- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 250 · 🔀 900 · 📦 3 · 📋 4K - 16% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 250 · 🔀 900 · 📦 3 · 📋 4K - 16% open · ⏱️ 03.07.2025): ``` git clone https://github.com/rdkit/rdkit ``` -- [PyPi](https://pypi.org/project/rdkit) (📥 960K / month · 📦 1.1K · ⏱️ 12.06.2025): +- [PyPi](https://pypi.org/project/rdkit) (📥 1M / month · 📦 1.1K · ⏱️ 12.06.2025): ``` pip install rdkit ``` @@ -994,12 +994,12 @@ _General tools for atomistic machine learning._
DeepChem (🥇34 · ⭐ 6.1K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT -- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 260 · 🔀 1.9K · 📦 640 · 📋 2K - 38% open · ⏱️ 09.06.2025): +- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 260 · 🔀 1.9K · 📦 640 · 📋 2K - 38% open · ⏱️ 02.07.2025): ``` git clone https://github.com/deepchem/deepchem ``` -- [PyPi](https://pypi.org/project/deepchem) (📥 37K / month · 📦 20 · ⏱️ 09.06.2025): +- [PyPi](https://pypi.org/project/deepchem) (📥 40K / month · 📦 20 · ⏱️ 02.07.2025): ``` pip install deepchem ``` @@ -1007,7 +1007,7 @@ _General tools for atomistic machine learning._ ``` conda install -c conda-forge deepchem ``` -- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 8.8K · ⭐ 5 · ⏱️ 09.06.2025): +- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 8.8K · ⭐ 5 · ⏱️ 02.07.2025): ``` docker pull deepchemio/deepchem ``` @@ -1019,27 +1019,15 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/hackingmaterials/matminer ``` -- [PyPi](https://pypi.org/project/matminer) (📥 29K / month · 📦 60 · ⏱️ 06.10.2024): +- [PyPi](https://pypi.org/project/matminer) (📥 31K / month · 📦 60 · ⏱️ 06.10.2024): ``` pip install matminer ``` -- [Conda](https://anaconda.org/conda-forge/matminer) (📥 92K · ⏱️ 22.04.2025): +- [Conda](https://anaconda.org/conda-forge/matminer) (📥 93K · ⏱️ 22.04.2025): ``` conda install -c conda-forge matminer ```
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MAML (🥈24 · ⭐ 420) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3 - -- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 39 · 🔀 86 · 📦 16 · 📋 76 - 15% open · ⏱️ 02.06.2025): - - ``` - git clone https://github.com/materialsvirtuallab/maml - ``` -- [PyPi](https://pypi.org/project/maml) (📥 360 / month · 📦 3 · ⏱️ 02.04.2025): - ``` - pip install maml - ``` -
QUIP (🥈24 · ⭐ 370) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran - [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 86 · 🔀 120 · 📥 760 · 📦 46 · 📋 480 - 23% open · ⏱️ 22.04.2025): @@ -1047,7 +1035,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/libAtoms/QUIP ``` -- [PyPi](https://pypi.org/project/quippy-ase) (📥 2.9K / month · 📦 4 · ⏱️ 15.01.2023): +- [PyPi](https://pypi.org/project/quippy-ase) (📥 3.1K / month · 📦 4 · ⏱️ 15.01.2023): ``` pip install quippy-ase ``` @@ -1056,14 +1044,26 @@ _General tools for atomistic machine learning._ docker pull libatomsquip/quip ```
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JARVIS-Tools (🥈23 · ⭐ 350 · 💤) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom +
MAML (🥈23 · ⭐ 420) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3 + +- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 39 · 🔀 86 · 📦 16 · 📋 76 - 15% open · ⏱️ 02.06.2025): + + ``` + git clone https://github.com/materialsvirtuallab/maml + ``` +- [PyPi](https://pypi.org/project/maml) (📥 420 / month · 📦 3 · ⏱️ 02.04.2025): + ``` + pip install maml + ``` +
+
JARVIS-Tools (🥈22 · ⭐ 350) - This repository is no longer maintained. For the latest updates and continued development, please visit:.. Custom -- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 130 · 📋 94 - 52% open · ⏱️ 20.11.2024): +- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 130 · 📋 94 - 52% open · ⏱️ 27.06.2025): ``` git clone https://github.com/usnistgov/jarvis ``` -- [PyPi](https://pypi.org/project/jarvis-tools) (📥 16K / month · 📦 35 · ⏱️ 24.06.2025): +- [PyPi](https://pypi.org/project/jarvis-tools) (📥 15K / month · 📦 35 · ⏱️ 24.06.2025): ``` pip install jarvis-tools ``` @@ -1072,14 +1072,14 @@ _General tools for atomistic machine learning._ conda install -c conda-forge jarvis-tools ```
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AtomAI (🥈22 · ⭐ 210 · 📈) - Deep and Machine Learning for Microscopy. MIT computer-vision USL experimental-data +
AtomAI (🥈22 · ⭐ 210) - Deep and Machine Learning for Microscopy. MIT computer-vision USL experimental-data - [GitHub](https://github.com/pycroscopy/atomai) (👨‍💻 6 · 🔀 41 · 📦 10 · 📋 20 - 55% open · ⏱️ 23.06.2025): ``` git clone https://github.com/pycroscopy/atomai ``` -- [PyPi](https://pypi.org/project/atomai) (📥 470 / month · 📦 1 · ⏱️ 23.06.2025): +- [PyPi](https://pypi.org/project/atomai) (📥 710 / month · 📦 1 · ⏱️ 23.06.2025): ``` pip install atomai ``` @@ -1091,7 +1091,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/datamol-io/molfeat ``` -- [PyPi](https://pypi.org/project/molfeat) (📥 3.1K / month · 📦 13 · ⏱️ 27.05.2025): +- [PyPi](https://pypi.org/project/molfeat) (📥 3.4K / month · 📦 13 · ⏱️ 27.05.2025): ``` pip install molfeat ``` @@ -1100,60 +1100,60 @@ _General tools for atomistic machine learning._ conda install -c conda-forge molfeat ```
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Scikit-Matter (🥈20 · ⭐ 83 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn +
Scikit-Matter (🥈21 · ⭐ 84) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn -- [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 18 · 🔀 22 · 📦 12 · 📋 78 - 23% open · ⏱️ 13.06.2025): +- [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 18 · 🔀 22 · 📦 14 · 📋 78 - 23% open · ⏱️ 30.06.2025): ``` git clone https://github.com/scikit-learn-contrib/scikit-matter ``` -- [PyPi](https://pypi.org/project/skmatter) (📥 1.8K / month · 📦 4 · ⏱️ 12.06.2025): +- [PyPi](https://pypi.org/project/skmatter) (📥 2.2K / month · 📦 4 · ⏱️ 30.06.2025): ``` pip install skmatter ``` -- [Conda](https://anaconda.org/conda-forge/skmatter) (📥 3.4K · ⏱️ 12.06.2025): +- [Conda](https://anaconda.org/conda-forge/skmatter) (📥 3.5K · ⏱️ 01.07.2025): ``` conda install -c conda-forge skmatter ```
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QML (🥈17 · ⭐ 200) - QML: Quantum Machine Learning. MIT +
QML (🥈17 · ⭐ 200 · 💤) - QML: Quantum Machine Learning. MIT - [GitHub](https://github.com/qmlcode/qml) (👨‍💻 10 · 🔀 84 · 📋 59 - 64% open · ⏱️ 08.12.2024): ``` git clone https://github.com/qmlcode/qml ``` -- [PyPi](https://pypi.org/project/qml) (📥 250 / month · ⏱️ 13.08.2018): +- [PyPi](https://pypi.org/project/qml) (📥 260 / month · ⏱️ 13.08.2018): ``` pip install qml ```
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Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 650) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules +
MLatom (🥉15 · ⭐ 95) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 32 · 🔀 75 · 📋 27 - 11% open · ⏱️ 15.06.2025): +- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 6 · 🔀 14 · 📋 7 - 28% open · ⏱️ 02.07.2025): ``` - git clone https://github.com/divelab/AIRS + git clone https://github.com/dralgroup/mlatom + ``` +- [PyPi](https://pypi.org/project/mlatom) (📥 900 / month · ⏱️ 02.07.2025): + ``` + pip install mlatom ```
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MAST-ML (🥉14 · ⭐ 120) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT +
Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 650) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules -- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 19 · 🔀 61 · 📥 140 · 📋 220 - 14% open · ⏱️ 15.04.2025): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 33 · 🔀 76 · 📋 27 - 11% open · ⏱️ 01.07.2025): ``` - git clone https://github.com/uw-cmg/MAST-ML + git clone https://github.com/divelab/AIRS ```
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MLatom (🥉14 · ⭐ 93) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization +
MAST-ML (🥉13 · ⭐ 120) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT -- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 6 · 🔀 14 · 📋 7 - 28% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 19 · 🔀 61 · 📥 140 · 📋 220 - 14% open · ⏱️ 15.04.2025): ``` - git clone https://github.com/dralgroup/mlatom - ``` -- [PyPi](https://pypi.org/project/mlatom) (📥 620 / month · ⏱️ 26.06.2025): - ``` - pip install mlatom + git clone https://github.com/uw-cmg/MAST-ML ```
Show 11 hidden projects... @@ -1161,7 +1161,7 @@ _General tools for atomistic machine learning._ - Automatminer (🥉16 · ⭐ 160 · 💀) - An automatic engine for predicting materials properties. Custom autoML - XenonPy (🥉15 · ⭐ 140 · 💀) - XenonPy is a Python Software for Materials Informatics. BSD-3 - AMPtorch (🥉11 · ⭐ 60 · 💀) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0 -- OpenChem (🥉10 · ⭐ 710 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT +- OpenChem (🥉10 · ⭐ 720 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT - JAXChem (🥉7 · ⭐ 80 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT - uncertainty_benchmarking (🥉7 · ⭐ 42 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic - torchchem (🥉7 · ⭐ 36 · 💀) - An experimental repo for experimenting with PyTorch models. MIT @@ -1178,42 +1178,30 @@ _General tools for atomistic machine learning._ _Projects that implement generative models for atomistic ML._ -
GT4SD (🥇16 · ⭐ 350) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pretrained drug-discovery rep-learn +
GT4SD (🥇16 · ⭐ 360) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pretrained drug-discovery rep-learn - [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 75 · 📋 120 - 11% open · ⏱️ 19.02.2025): ``` git clone https://github.com/GT4SD/gt4sd-core ``` -- [PyPi](https://pypi.org/project/gt4sd) (📥 940 / month · ⏱️ 19.02.2025): +- [PyPi](https://pypi.org/project/gt4sd) (📥 800 / month · ⏱️ 19.02.2025): ``` pip install gt4sd ```
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synspace (🥇15 · ⭐ 45) - Synthesis generative model. MIT +
synspace (🥇14 · ⭐ 45) - Synthesis generative model. MIT - [GitHub](https://github.com/whitead/synspace) (👨‍💻 2 · 🔀 4 · 📦 36 · 📋 4 - 50% open · ⏱️ 24.04.2025): ``` git clone https://github.com/whitead/synspace ``` -- [PyPi](https://pypi.org/project/synspace) (📥 4.6K / month · 📦 4 · ⏱️ 24.04.2025): +- [PyPi](https://pypi.org/project/synspace) (📥 5.2K / month · 📦 4 · ⏱️ 24.04.2025): ``` pip install synspace ```
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PMTransformer (🥈14 · ⭐ 100 · 💤) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer - -- [GitHub](https://github.com/hspark1212/MOFTransformer) (👨‍💻 2 · 🔀 15 · 📦 8 · 📋 44 - 2% open · ⏱️ 20.06.2024): - - ``` - git clone https://github.com/hspark1212/MOFTransformer - ``` -- [PyPi](https://pypi.org/project/moftransformer) (📥 580 / month · 📦 1 · ⏱️ 20.06.2024): - ``` - pip install moftransformer - ``` -
SLICES and MatterGPT (🥈13 · ⭐ 110) - SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT,.. LGPL-2.1 rep-eng language-models transformer materials-discovery structure-prediction - [GitHub](https://github.com/xiaohang007/SLICES) (👨‍💻 1 · 🔀 43 · 📦 5 · 📋 17 - 23% open · ⏱️ 26.03.2025): @@ -1221,7 +1209,7 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/xiaohang007/SLICES ``` -- [PyPi](https://pypi.org/project/slices) (📥 380 / month · 📦 1 · ⏱️ 01.03.2025): +- [PyPi](https://pypi.org/project/slices) (📥 340 / month · 📦 1 · ⏱️ 01.03.2025): ``` pip install slices ``` @@ -1238,21 +1226,22 @@ _Projects that implement generative models for atomistic ML._ git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet ```
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SiMGen (🥈10 · ⭐ 20) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz +
SiMGen (🥈11 · ⭐ 20) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz - [GitHub](https://github.com/RokasEl/simgen) (👨‍💻 4 · 🔀 3 · 📦 2 · 📋 4 - 25% open · ⏱️ 27.04.2025): ``` git clone https://github.com/RokasEl/simgen ``` -- [PyPi](https://pypi.org/project/simgen) (📥 18 / month · ⏱️ 13.12.2024): +- [PyPi](https://pypi.org/project/simgen) (📥 25 / month · ⏱️ 13.12.2024): ``` pip install simgen ```
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Show 11 hidden projects... +
Show 12 hidden projects... -- MoLeR (🥈14 · ⭐ 300 · 💀) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT +- MoLeR (🥇14 · ⭐ 300 · 💀) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT +- PMTransformer (🥇14 · ⭐ 100 · 💀) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer - EDM (🥉9 · ⭐ 510 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT - G-SchNet (🥉8 · ⭐ 140 · 💀) - G-SchNet - a generative model for 3d molecular structures. MIT - bVAE-IM (🥉8 · ⭐ 12 · 💀) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper @@ -1272,18 +1261,18 @@ _Projects that implement generative models for atomistic ML._ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics._ -
NequIP (🥇31 · ⭐ 740 · 📈) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +
NequIP (🥇32 · ⭐ 740 · 📈) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT -- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 20 · 🔀 160 · 📦 39 · 📋 110 - 9% open · ⏱️ 23.06.2025): +- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 21 · 🔀 160 · 📦 39 · 📋 110 - 6% open · ⏱️ 01.07.2025): ``` git clone https://github.com/mir-group/nequip ``` -- [PyPi](https://pypi.org/project/nequip) (📥 99K / month · 📦 13 · ⏱️ 23.06.2025): +- [PyPi](https://pypi.org/project/nequip) (📥 61K / month · 📦 13 · ⏱️ 01.07.2025): ``` pip install nequip ``` -- [Conda](https://anaconda.org/conda-forge/nequip) (📥 11K · ⏱️ 23.06.2025): +- [Conda](https://anaconda.org/conda-forge/nequip) (📥 11K · ⏱️ 01.07.2025): ``` conda install -c conda-forge nequip ``` @@ -1295,7 +1284,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/deepmodeling/deepmd-kit ``` -- [PyPi](https://pypi.org/project/deepmd-kit) (📥 11K / month · 📦 11 · ⏱️ 11.06.2025): +- [PyPi](https://pypi.org/project/deepmd-kit) (📥 14K / month · 📦 11 · ⏱️ 11.06.2025): ``` pip install deepmd-kit ``` @@ -1303,24 +1292,24 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` conda install -c deepmodeling deepmd-kit ``` -- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.7K · ⭐ 1 · ⏱️ 12.06.2025): +- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.8K · ⭐ 1 · ⏱️ 12.06.2025): ``` docker pull deepmodeling/deepmd-kit ```
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fairchem (🥇29 · ⭐ 1.5K) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +
fairchem (🥇30 · ⭐ 1.6K · 📈) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis -- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 53 · 🔀 330 · 📋 390 - 6% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 54 · 🔀 350 · 📋 400 - 4% open · ⏱️ 02.07.2025): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 100K / month · 📦 10 · ⏱️ 03.06.2025): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 65K / month · 📦 12 · ⏱️ 01.07.2025): ``` pip install fairchem-core ```
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MACE (🥇23 · ⭐ 760) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT +
MACE (🥇23 · ⭐ 770) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT - [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 59 · 🔀 280 · 📋 450 - 21% open · ⏱️ 20.06.2025): @@ -1328,38 +1317,38 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/ACEsuit/mace ```
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TorchMD-NET (🥇22 · ⭐ 410) - Training neural network potentials. MIT MD rep-learn transformer pretrained +
TorchMD-NET (🥇22 · ⭐ 420) - Training neural network potentials. MIT MD rep-learn transformer pretrained -- [GitHub](https://github.com/torchmd/torchmd-net) (👨‍💻 17 · 🔀 84 · 📥 130 · 📋 130 - 33% open · ⏱️ 05.05.2025): +- [GitHub](https://github.com/torchmd/torchmd-net) (👨‍💻 17 · 🔀 84 · 📥 140 · 📋 130 - 33% open · ⏱️ 05.05.2025): ``` git clone https://github.com/torchmd/torchmd-net ``` -- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 510K · ⏱️ 30.05.2025): +- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 520K · ⏱️ 30.05.2025): ``` conda install -c conda-forge torchmd-net ```
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MatCalc (🥇22 · ⭐ 95) - A python library for calculating materials properties from the PES. BSD-3 workflows benchmarking UIP pretrained model-repository +
MatCalc (🥇21 · ⭐ 95) - A python library for calculating materials properties from the PES. BSD-3 workflows benchmarking UIP pretrained model-repository - [GitHub](https://github.com/materialsvirtuallab/matcalc) (👨‍💻 17 · 🔀 23 · 📦 10 · 📋 11 - 9% open · ⏱️ 24.06.2025): ``` git clone https://github.com/materialsvirtuallab/matcalc ``` -- [PyPi](https://pypi.org/project/matcalc) (📥 5.6K / month · 📦 6 · ⏱️ 10.05.2025): +- [PyPi](https://pypi.org/project/matcalc) (📥 4.6K / month · 📦 6 · ⏱️ 10.05.2025): ``` pip install matcalc ```
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KLIFF (🥈21 · ⭐ 36) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows +
KLIFF (🥇21 · ⭐ 37) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows - [GitHub](https://github.com/openkim/kliff) (👨‍💻 14 · 🔀 21 · 📦 4 · 📋 57 - 42% open · ⏱️ 02.06.2025): ``` git clone https://github.com/openkim/kliff ``` -- [PyPi](https://pypi.org/project/kliff) (📥 76 / month · ⏱️ 11.04.2025): +- [PyPi](https://pypi.org/project/kliff) (📥 89 / month · ⏱️ 11.04.2025): ``` pip install kliff ``` @@ -1368,58 +1357,58 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc conda install -c conda-forge kliff ```
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janus-core (🥈21 · ⭐ 31) - Tools for machine learnt interatomic potentials. BSD-3 benchmarking workflows structure-optimization MD transport-phenomena +
janus-core (🥇21 · ⭐ 32) - Tools for machine learnt interatomic potentials. BSD-3 benchmarking workflows structure-optimization MD transport-phenomena - [GitHub](https://github.com/stfc/janus-core) (👨‍💻 9 · 🔀 12 · 📥 150 · 📦 10 · 📋 250 - 15% open · ⏱️ 26.06.2025): ``` git clone https://github.com/stfc/janus-core ``` -- [PyPi](https://pypi.org/project/janus-core) (📥 590 / month · 📦 3 · ⏱️ 13.06.2025): +- [PyPi](https://pypi.org/project/janus-core) (📥 610 / month · 📦 3 · ⏱️ 13.06.2025): ``` pip install janus-core ```
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Allegro (🥈20 · ⭐ 400) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT - -- [GitHub](https://github.com/mir-group/allegro) (👨‍💻 7 · 🔀 57 · 📋 47 - 12% open · ⏱️ 22.06.2025): - - ``` - git clone https://github.com/mir-group/allegro - ``` -
Metatrain (🥈20 · ⭐ 33) - Training and evaluating machine learning models for atomistic systems. BSD-3 workflows benchmarking rep-eng rep-learn -- [GitHub](https://github.com/metatensor/metatrain) (👨‍💻 17 · 🔀 11 · 📥 12 · 📦 6 · 📋 200 - 35% open · ⏱️ 24.06.2025): +- [GitHub](https://github.com/metatensor/metatrain) (👨‍💻 17 · 🔀 11 · 📥 12 · 📦 6 · 📋 200 - 34% open · ⏱️ 03.07.2025): ``` git clone https://github.com/metatensor/metatrain ``` -- [PyPi](https://pypi.org/project/metatrain) (📥 1.9K / month · 📦 2 · ⏱️ 11.06.2025): +- [PyPi](https://pypi.org/project/metatrain) (📥 2K / month · 📦 2 · ⏱️ 11.06.2025): ``` pip install metatrain ```
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sGDML (🥈19 · ⭐ 150 · 📈) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT +
Allegro (🥈19 · ⭐ 410) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT + +- [GitHub](https://github.com/mir-group/allegro) (👨‍💻 7 · 🔀 57 · 📋 45 - 8% open · ⏱️ 01.07.2025): + + ``` + git clone https://github.com/mir-group/allegro + ``` +
+
sGDML (🥈19 · ⭐ 150) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT - [GitHub](https://github.com/stefanch/sGDML) (👨‍💻 8 · 🔀 37 · 📦 13 · 📋 22 - 50% open · ⏱️ 13.06.2025): ``` git clone https://github.com/stefanch/sGDML ``` -- [PyPi](https://pypi.org/project/sgdml) (📥 260 / month · 📦 2 · ⏱️ 13.06.2025): +- [PyPi](https://pypi.org/project/sgdml) (📥 450 / month · 📦 2 · ⏱️ 13.06.2025): ``` pip install sgdml ```
apax (🥈19 · ⭐ 20) - A flexible and performant framework for training machine learning potentials. MIT -- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 9 · 🔀 3 · 📦 4 · 📋 150 - 9% open · ⏱️ 25.06.2025): +- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 9 · 🔀 3 · 📦 4 · 📋 150 - 10% open · ⏱️ 01.07.2025): ``` git clone https://github.com/apax-hub/apax ``` -- [PyPi](https://pypi.org/project/apax) (📥 390 / month · ⏱️ 17.06.2025): +- [PyPi](https://pypi.org/project/apax) (📥 410 / month · ⏱️ 17.06.2025): ``` pip install apax ``` @@ -1431,19 +1420,19 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/jla-gardner/graph-pes ``` -- [PyPi](https://pypi.org/project/graph-pes) (📥 2K / month · 📦 2 · ⏱️ 25.06.2025): +- [PyPi](https://pypi.org/project/graph-pes) (📥 1.9K / month · 📦 2 · ⏱️ 25.06.2025): ``` pip install graph-pes ```
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Autoplex (🥈17 · ⭐ 81) - Code for automated fitting of machine learned interatomic potentials. GPL-3.0 benchmarking workflows +
Autoplex (🥈17 · ⭐ 85) - Code for automated fitting of machine learned interatomic potentials. GPL-3.0 benchmarking workflows -- [GitHub](https://github.com/autoatml/autoplex) (👨‍💻 12 · 🔀 14 · 📦 2 · 📋 130 - 27% open · ⏱️ 25.06.2025): +- [GitHub](https://github.com/autoatml/autoplex) (👨‍💻 12 · 🔀 14 · 📦 2 · 📋 130 - 27% open · ⏱️ 01.07.2025): ``` git clone https://github.com/autoatml/autoplex ``` -- [PyPi](https://pypi.org/project/autoplex) (📥 92 / month · ⏱️ 28.04.2025): +- [PyPi](https://pypi.org/project/autoplex) (📥 100 / month · ⏱️ 01.07.2025): ``` pip install autoplex ``` @@ -1456,40 +1445,40 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/learningmatter-mit/NeuralForceField ```
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NNPOps (🥈15 · ⭐ 93) - High-performance operations for neural network potentials. MIT MD C++ +
MLIPX - Machine-Learned Interatomic Potential eXploration (🥈15 · ⭐ 88) - Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned.. MIT benchmarking viz workflows -- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 10 · 🔀 18 · 📋 57 - 38% open · ⏱️ 28.02.2025): +- [GitHub](https://github.com/basf/mlipx) (👨‍💻 5 · 🔀 7 · 📦 2 · 📋 14 - 14% open · ⏱️ 01.07.2025): ``` - git clone https://github.com/openmm/NNPOps + git clone https://github.com/basf/mlipx ``` -- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 500K · ⏱️ 22.04.2025): +- [PyPi](https://pypi.org/project/mlipx) (📥 180 / month · ⏱️ 09.06.2025): ``` - conda install -c conda-forge nnpops + pip install mlipx ```
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MLIPX - Machine-Learned Interatomic Potential eXploration (🥈15 · ⭐ 87) - Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned.. MIT benchmarking viz workflows +
Ultra-Fast Force Fields (UF3) (🥈15 · ⭐ 68 · 💤) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2 -- [GitHub](https://github.com/basf/mlipx) (👨‍💻 5 · 🔀 7 · 📦 2 · 📋 13 - 15% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 26 · 📦 2 · 📋 51 - 37% open · ⏱️ 04.10.2024): ``` - git clone https://github.com/basf/mlipx + git clone https://github.com/uf3/uf3 ``` -- [PyPi](https://pypi.org/project/mlipx) (📥 160 / month · ⏱️ 09.06.2025): +- [PyPi](https://pypi.org/project/uf3) (📥 26 / month · ⏱️ 27.10.2023): ``` - pip install mlipx + pip install uf3 ```
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Ultra-Fast Force Fields (UF3) (🥈15 · ⭐ 68 · 💤) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2 +
NNPOps (🥈14 · ⭐ 93) - High-performance operations for neural network potentials. MIT MD C++ -- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 26 · 📦 2 · 📋 51 - 37% open · ⏱️ 04.10.2024): +- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 10 · 🔀 18 · 📋 57 - 38% open · ⏱️ 28.02.2025): ``` - git clone https://github.com/uf3/uf3 + git clone https://github.com/openmm/NNPOps ``` -- [PyPi](https://pypi.org/project/uf3) (📥 23 / month · ⏱️ 27.10.2023): +- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 510K · ⏱️ 22.04.2025): ``` - pip install uf3 + conda install -c conda-forge nnpops ```
n2p2 (🥈13 · ⭐ 240) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++ @@ -1502,32 +1491,20 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
So3krates (MLFF) (🥈13 · ⭐ 120 · 💤) - Build neural networks for machine learning force fields with JAX. MIT -- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 27 · 📋 13 - 46% open · ⏱️ 23.08.2024): +- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 28 · 📋 13 - 46% open · ⏱️ 23.08.2024): ``` git clone https://github.com/thorben-frank/mlff ```
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PiNN (🥈12 · ⭐ 120) - A Python library for building atomic neural networks. BSD-3 +
Pacemaker (🥈12 · ⭐ 87 · 💤) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom -- [GitHub](https://github.com/Teoroo-CMC/PiNN) (👨‍💻 6 · 🔀 35 · 📋 7 - 14% open · ⏱️ 17.02.2025): - - ``` - git clone https://github.com/Teoroo-CMC/PiNN - ``` -- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (📥 470 · ⏱️ 17.02.2025): - ``` - docker pull teoroo/pinn - ``` -
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Pacemaker (🥈12 · ⭐ 86 · 💤) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom - -- [GitHub](https://github.com/ICAMS/python-ace) (👨‍💻 7 · 🔀 21 · 📋 60 - 35% open · ⏱️ 20.11.2024): +- [GitHub](https://github.com/ICAMS/python-ace) (👨‍💻 7 · 🔀 21 · 📋 60 - 33% open · ⏱️ 20.11.2024): ``` git clone https://github.com/ICAMS/python-ace ``` -- [PyPi](https://pypi.org/project/python-ace) (📥 7 / month · ⏱️ 24.10.2022): +- [PyPi](https://pypi.org/project/python-ace) (📥 9 / month · ⏱️ 24.10.2022): ``` pip install python-ace ``` @@ -1552,20 +1529,24 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://gitlab.com/materials-modeling/calorine ```
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Point Edge Transformer (PET) (🥈10 · ⭐ 28) - Point Edge Transformer. MIT rep-learn transformer +
PiNN (🥈11 · ⭐ 120) - A Python library for building atomic neural networks. BSD-3 -- [GitHub](https://github.com/spozdn/pet) (👨‍💻 9 · 🔀 7 · ⏱️ 18.03.2025): +- [GitHub](https://github.com/Teoroo-CMC/PiNN) (👨‍💻 6 · 🔀 35 · 📋 7 - 14% open · ⏱️ 17.02.2025): ``` - git clone https://github.com/spozdn/pet + git clone https://github.com/Teoroo-CMC/PiNN + ``` +- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (📥 480 · ⏱️ 17.02.2025): + ``` + docker pull teoroo/pinn ```
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ACE1.jl (🥈10 · ⭐ 22) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia +
Point Edge Transformer (PET) (🥈10 · ⭐ 29) - Point Edge Transformer. MIT rep-learn transformer -- [GitHub](https://github.com/ACEsuit/ACE1.jl) (👨‍💻 9 · 🔀 7 · 📋 46 - 47% open · ⏱️ 15.04.2025): +- [GitHub](https://github.com/spozdn/pet) (👨‍💻 9 · 🔀 7 · ⏱️ 18.03.2025): ``` - git clone https://github.com/ACEsuit/ACE1.jl + git clone https://github.com/spozdn/pet ```
tinker-hp (🥉9 · ⭐ 92) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom @@ -1576,7 +1557,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/TinkerTools/tinker-hp ```
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ACE.jl (🥉9 · ⭐ 65) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia +
ACE.jl (🥉9 · ⭐ 65 · 💤) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia - [GitHub](https://github.com/ACEsuit/ACE.jl) (👨‍💻 12 · 🔀 14 · 📋 82 - 29% open · ⏱️ 17.12.2024): @@ -1592,12 +1573,12 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/deepmodeling/deepmd-gnn ```
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ALF (🥉9 · ⭐ 36) - A framework for performing active learning for training machine-learned interatomic potentials. Custom active-learning +
ACE1.jl (🥉9 · ⭐ 22) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia -- [GitHub](https://github.com/lanl/ALF) (👨‍💻 5 · 🔀 12 · ⏱️ 28.03.2025): +- [GitHub](https://github.com/ACEsuit/ACE1.jl) (👨‍💻 9 · 🔀 7 · 📋 46 - 47% open · ⏱️ 15.04.2025): ``` - git clone https://github.com/lanl/alf + git clone https://github.com/ACEsuit/ACE1.jl ```
ACEfit (🥉9 · ⭐ 7 · 💤) - MIT Julia @@ -1616,7 +1597,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/atomicarchitects/equiformer_v2 ```
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PyNEP (🥉8 · ⭐ 54) - A python interface of the machine learning potential NEP used in GPUMD. MIT +
PyNEP (🥉8 · ⭐ 54 · 💤) - A python interface of the machine learning potential NEP used in GPUMD. MIT - [GitHub](https://github.com/bigd4/PyNEP) (👨‍💻 9 · 🔀 17 · 📋 13 - 38% open · ⏱️ 15.12.2024): @@ -1624,12 +1605,12 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/bigd4/PyNEP ```
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GAP (🥉8 · ⭐ 42) - Gaussian Approximation Potential (GAP). Custom +
ALF (🥉8 · ⭐ 36) - A framework for performing active learning for training machine-learned interatomic potentials. Custom active-learning -- [GitHub](https://github.com/libAtoms/GAP) (👨‍💻 13 · 🔀 20 · ⏱️ 22.04.2025): +- [GitHub](https://github.com/lanl/ALF) (👨‍💻 5 · 🔀 12 · ⏱️ 28.03.2025): ``` - git clone https://github.com/libAtoms/GAP + git clone https://github.com/lanl/alf ```
TurboGAP (🥉8 · ⭐ 17) - The TurboGAP code. Custom Fortran @@ -1640,7 +1621,15 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/mcaroba/turbogap ```
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Asparagus (🥉7 · ⭐ 11) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD +
GAP (🥉7 · ⭐ 42) - Gaussian Approximation Potential (GAP). Custom + +- [GitHub](https://github.com/libAtoms/GAP) (👨‍💻 13 · 🔀 20 · ⏱️ 22.04.2025): + + ``` + git clone https://github.com/libAtoms/GAP + ``` +
+
Asparagus (🥉6 · ⭐ 11) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD - [GitHub](https://github.com/MMunibas/Asparagus) (👨‍💻 9 · 🔀 5 · ⏱️ 09.04.2025): @@ -1656,7 +1645,15 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/RowleyGroup/MLXDM ```
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MatML (🥉6 · ⭐ 7 · 🐣) - Full MatML Docker image, including MatGL, MatCalc, MatPES and LAMMPS with ML-GNNP and ML-SNAP. BSD-3 MD UIP rep-learn pretrained +
TensorPotential (🥉5 · ⭐ 10 · 💤) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom + +- [GitHub](https://github.com/ICAMS/TensorPotential) (👨‍💻 4 · 🔀 5 · ⏱️ 12.09.2024): + + ``` + git clone https://github.com/ICAMS/TensorPotential + ``` +
+
MatML (🥉5 · ⭐ 7 · 🐣) - Full MatML Docker image, including MatGL, MatCalc, MatPES and LAMMPS with ML-GNNP and ML-SNAP. BSD-3 MD UIP rep-learn pretrained - [GitHub](https://github.com/materialsvirtuallab/matml) (👨‍💻 2 · ⏱️ 02.06.2025): @@ -1668,37 +1665,29 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc docker pull materialsvirtuallab/matml ```
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TensorPotential (🥉5 · ⭐ 10 · 💤) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom - -- [GitHub](https://github.com/ICAMS/TensorPotential) (👨‍💻 4 · 🔀 5 · ⏱️ 12.09.2024): - - ``` - git clone https://github.com/ICAMS/TensorPotential - ``` -
Show 39 hidden projects... -- TorchANI (🥇24 · ⭐ 500 · 💀) - Accurate Neural Network Potential on PyTorch. MIT -- MEGNet (🥇22 · ⭐ 530 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3 multifidelity +- TorchANI (🥇23 · ⭐ 500 · 💀) - Accurate Neural Network Potential on PyTorch. MIT +- MEGNet (🥇22 · ⭐ 540 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3 multifidelity - PyXtalFF (🥈14 · ⭐ 90 · 💀) - Machine Learning Interatomic Potential Predictions. MIT - TensorMol (🥈12 · ⭐ 270 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper - ANI-1 (🥈12 · ⭐ 220 · 💀) - ANI-1 neural net potential with python interface (ASE). MIT -- SIMPLE-NN (🥈11 · ⭐ 47 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0 +- SIMPLE-NN (🥈11 · ⭐ 48 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0 - CCS_fit (🥈10 · ⭐ 10 · 💀) - Curvature Constrained Splines. GPL-3.0 - aiida-mlip (🥈10 · ⭐ 1) - machine learning interatomic potentials aiida plugin. BSD-3 workflows structure-optimization MD - DimeNet (🥉9 · ⭐ 320 · 💀) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom - SchNet (🥉9 · ⭐ 250 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT - GemNet (🥉9 · ⭐ 200 · 💀) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom -- MACE-Jax (🥉9 · ⭐ 73 · 💀) - Equivariant machine learning interatomic potentials in JAX. MIT +- NNsforMD (🥉9 · ⭐ 11 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT - AIMNet (🥉8 · ⭐ 100 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper -- SIMPLE-NN v2 (🥉8 · ⭐ 42 · 💀) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0 +- MACE-Jax (🥉8 · ⭐ 73 · 💀) - Equivariant machine learning interatomic potentials in JAX. MIT +- SIMPLE-NN v2 (🥉8 · ⭐ 43 · 💀) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0 - Atomistic Adversarial Attacks (🥉8 · ⭐ 38 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic - SNAP (🥉8 · ⭐ 36 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3 -- NNsforMD (🥉8 · ⭐ 11 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT - MEGNetSparse (🥉8 · ⭐ 4 · 💤) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect - PhysNet (🥉7 · ⭐ 100 · 💀) - Code for training PhysNet models. MIT electrostatics - BPNET (🥉7 · ⭐ 3 · 🐣) - Fast Behler-Parrinello type neural networks in Fortran2008. MIT rep-eng Fortran -- MLIP-3 (🥉6 · ⭐ 23 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++ +- MLIP-3 (🥉6 · ⭐ 24 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++ - testing-framework (🥉6 · ⭐ 11 · 💀) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. Unlicensed benchmarking - PANNA (🥉6 · ⭐ 11 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking - NequIP-JAX (🥉5 · ⭐ 23 · 💀) - JAX implementation of the NequIP interatomic potential. Unlicensed @@ -1728,14 +1717,14 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce 🔗 MaCBench Leaderboard - Leaderboard for multimodal language models for chemistry & materials research. community-resource benchmarking datasets -
ChemBench (🥇19 · ⭐ 96) - How good are LLMs at chemistry?. MIT benchmarking multimodal +
ChemBench (🥇19 · ⭐ 97) - How good are LLMs at chemistry?. MIT benchmarking multimodal - [GitHub](https://github.com/lamalab-org/chembench) (👨‍💻 13 · 🔀 12 · 📦 3 · 📋 330 - 15% open · ⏱️ 03.06.2025): ``` git clone https://github.com/lamalab-org/chembench ``` -- [PyPi](https://pypi.org/project/chembench) (📥 3K / month · ⏱️ 27.02.2025): +- [PyPi](https://pypi.org/project/chembench) (📥 4K / month · ⏱️ 27.02.2025): ``` pip install chembench ``` @@ -1747,7 +1736,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/whitead/paper-qa ``` -- [PyPi](https://pypi.org/project/paper-qa) (📥 24K / month · 📦 13 · ⏱️ 28.05.2025): +- [PyPi](https://pypi.org/project/paper-qa) (📥 14K / month · 📦 13 · ⏱️ 28.05.2025): ``` pip install paper-qa ``` @@ -1759,60 +1748,60 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/OpenBioML/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 74 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 71 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ```
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ChemCrow (🥈16 · ⭐ 780) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent +
ChemCrow (🥈16 · ⭐ 780 · 💤) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent - [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 120 · 📦 10 · 📋 24 - 37% open · ⏱️ 19.12.2024): ``` git clone https://github.com/ur-whitelab/chemcrow-public ``` -- [PyPi](https://pypi.org/project/chemcrow) (📥 410 / month · ⏱️ 27.03.2024): +- [PyPi](https://pypi.org/project/chemcrow) (📥 440 / month · ⏱️ 27.03.2024): ``` pip install chemcrow ```
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AtomGPT (🥈14 · ⭐ 67) - Generative Pretrained Transformer Models for Materials Design https://www.youtube.com/@dr_k_choudhary. Custom generative pretrained transformer +
AtomGPT (🥈13 · ⭐ 67) - This repository is no longer maintained. For the latest updates and continued development, please visit:.. Custom generative pretrained transformer -- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 6 · 🔀 15 · 📦 3 · ⏱️ 02.06.2025): +- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 6 · 🔀 15 · ⏱️ 27.06.2025): ``` git clone https://github.com/usnistgov/atomgpt ``` -- [PyPi](https://pypi.org/project/atomgpt) (📥 110 / month · 📦 1 · ⏱️ 22.03.2025): +- [PyPi](https://pypi.org/project/atomgpt) (📥 120 / month · 📦 1 · ⏱️ 22.03.2025): ``` pip install atomgpt ```
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ChatMOF (🥈11 · ⭐ 84) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative +
ChatMOF (🥈11 · ⭐ 85) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative - [GitHub](https://github.com/Yeonghun1675/ChatMOF) (👨‍💻 2 · 🔀 18 · 📦 3 · ⏱️ 15.05.2025): ``` git clone https://github.com/Yeonghun1675/ChatMOF ``` -- [PyPi](https://pypi.org/project/chatmof) (📥 210 / month · ⏱️ 01.07.2024): +- [PyPi](https://pypi.org/project/chatmof) (📥 200 / month · ⏱️ 01.07.2024): ``` pip install chatmof ```
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NIST ChemNLP (🥈11 · ⭐ 76 · 💤) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. MIT literature-data +
NIST ChemNLP (🥉10 · ⭐ 76) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. MIT literature-data -- [GitHub](https://github.com/usnistgov/chemnlp) (👨‍💻 2 · 🔀 20 · 📦 4 · ⏱️ 19.08.2024): +- [GitHub](https://github.com/usnistgov/chemnlp) (👨‍💻 2 · 🔀 20 · ⏱️ 27.06.2025): ``` git clone https://github.com/usnistgov/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 74 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 71 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ```
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LLaMP (🥉8 · ⭐ 82 · 💤) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 multimodal RAG materials-discovery pretrained JavaScript Python +
LLaMP (🥉8 · ⭐ 84 · 💤) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 multimodal RAG materials-discovery pretrained JavaScript Python - [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 13 · 📋 25 - 32% open · ⏱️ 14.10.2024): @@ -1820,15 +1809,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce git clone https://github.com/chiang-yuan/llamp ```
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crystal-text-llm (🥉6 · ⭐ 100 · 💤) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery - -- [GitHub](https://github.com/facebookresearch/crystal-text-llm) (👨‍💻 3 · 🔀 22 · 📋 14 - 85% open · ⏱️ 18.06.2024): - - ``` - git clone https://github.com/facebookresearch/crystal-text-llm - ``` -
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LLM4Chem (🥉6 · ⭐ 89) - Official code repo for the paper LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,.. MIT cheminformatics datasets +
LLM4Chem (🥉6 · ⭐ 90) - Official code repo for the paper LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,.. MIT cheminformatics datasets - [GitHub](https://github.com/OSU-NLP-Group/LLM4Chem) (👨‍💻 2 · 🔀 13 · ⏱️ 09.06.2025): @@ -1852,7 +1833,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce git clone https://github.com/lamm-mit/Cephalo ```
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Show 12 hidden projects... +
Show 13 hidden projects... - ChemDataExtractor (🥈16 · ⭐ 330 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data - mat2vec (🥈12 · ⭐ 630 · 💀) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn @@ -1862,6 +1843,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce - MolSkill (🥉9 · ⭐ 110 · 💀) - Extracting medicinal chemistry intuition via preference machine learning. MIT drug-discovery recommender - chemlift (🥉7 · ⭐ 42 · 💀) - Language-interfaced fine-tuning for chemistry. MIT - LLM-Prop (🥉7 · ⭐ 39 · 💀) - A repository for the LLM-Prop implementation. MIT +- crystal-text-llm (🥉6 · ⭐ 110 · 💀) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery - BERT-PSIE-TC (🥉6 · ⭐ 15 · 💀) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism - MAPI_LLM (🥉5 · ⭐ 9 · 💀) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT ai-agent dataset - CatBERTa (🥉4 · ⭐ 26 · 💀) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis @@ -1875,23 +1857,23 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce _Projects that implement materials discovery methods using atomistic ML._ -
SMACT (🥇26 · ⭐ 110) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics +
SMACT (🥇23 · ⭐ 110 · 📉) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics -- [GitHub](https://github.com/WMD-group/SMACT) (👨‍💻 46 · 🔀 26 · 📦 58 · 📋 63 - 9% open · ⏱️ 07.04.2025): +- [GitHub](https://github.com/WMD-group/SMACT) (👨‍💻 46 · 🔀 27 · 📦 58 · 📋 63 - 9% open · ⏱️ 07.04.2025): ``` git clone https://github.com/WMD-group/SMACT ``` -- [PyPi](https://pypi.org/project/smact) (📥 3.7K / month · 📦 5 · ⏱️ 02.04.2025): +- [PyPi](https://pypi.org/project/smact) (📥 4.1K / month · 📦 5 · ⏱️ 02.04.2025): ``` pip install smact ``` -- [Conda](https://anaconda.org/conda-forge/smact) (📥 5.6K · ⏱️ 22.04.2025): +- [Conda](https://anaconda.org/conda-forge/smact) (📥 5.8K · ⏱️ 22.04.2025): ``` conda install -c conda-forge smact ```
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MatterGen (🥇18 · ⭐ 1.4K) - Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table.. MIT generative structure-prediction pretrained +
MatterGen (🥇17 · ⭐ 1.4K) - Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table.. MIT generative structure-prediction pretrained - [GitHub](https://github.com/microsoft/mattergen) (👨‍💻 9 · 🔀 230 · ⏱️ 13.06.2025): @@ -1899,7 +1881,7 @@ _Projects that implement materials discovery methods using atomistic ML._ git clone https://github.com/microsoft/mattergen ```
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aviary (🥈14 · ⭐ 57) - The Wren sits on its Roost in the Aviary. MIT +
aviary (🥈13 · ⭐ 57) - The Wren sits on its Roost in the Aviary. MIT - [GitHub](https://github.com/CompRhys/aviary) (👨‍💻 6 · 🔀 13 · 📋 33 - 12% open · ⏱️ 19.04.2025): @@ -1917,7 +1899,7 @@ _Projects that implement materials discovery methods using atomistic ML._
BOSS (🥈11 · ⭐ 23 · 💤) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic -- [PyPi](https://pypi.org/project/aalto-boss) (📥 400 / month · ⏱️ 13.11.2024): +- [PyPi](https://pypi.org/project/aalto-boss) (📥 420 / month · ⏱️ 13.11.2024): ``` pip install aalto-boss ``` @@ -1929,7 +1911,7 @@ _Projects that implement materials discovery methods using atomistic ML._
AGOX (🥉10 · ⭐ 15) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization -- [PyPi](https://pypi.org/project/agox) (📥 150 / month · 📦 1 · ⏱️ 24.06.2025): +- [PyPi](https://pypi.org/project/agox) (📥 330 / month · 📦 1 · ⏱️ 24.06.2025): ``` pip install agox ``` @@ -1939,7 +1921,7 @@ _Projects that implement materials discovery methods using atomistic ML._ git clone https://gitlab.com/agox/agox ```
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CSPML (crystal structure prediction with machine learning-based element substitution) (🥉5 · ⭐ 24) - Original implementation of CSPML. MIT structure-prediction +
CSPML (crystal structure prediction with machine learning-based element substitution) (🥉5 · ⭐ 24 · 💤) - Original implementation of CSPML. MIT structure-prediction - [GitHub](https://github.com/Minoru938/CSPML) (👨‍💻 1 · 🔀 8 · 📋 3 - 66% open · ⏱️ 22.12.2024): @@ -1950,10 +1932,10 @@ _Projects that implement materials discovery methods using atomistic ML._
Show 6 hidden projects... - Computational Autonomy for Materials Discovery (CAMD) (🥉7 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2 -- MAGUS (🥉4 · ⭐ 75 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning +- MAGUS (🥉4 · ⭐ 76 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning +- SPINNER (🥉4 · ⭐ 14 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction - ML-atomate (🥉4 · ⭐ 6 · 💀) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows - closed-loop-acceleration-benchmarks (🥉4 · 💀) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper -- SPINNER (🥉3 · ⭐ 13 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction - sl_discovery (🥉3 · ⭐ 5 · 💀) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper

@@ -1966,63 +1948,63 @@ _Projects that implement mathematical objects used in atomistic machine learning
KFAC-JAX (🥇20 · ⭐ 280) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2 -- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 19 · 🔀 27 · 📦 11 · 📋 30 - 63% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 19 · 🔀 27 · 📦 11 · 📋 30 - 63% open · ⏱️ 02.07.2025): ``` git clone https://github.com/google-deepmind/kfac-jax ``` -- [PyPi](https://pypi.org/project/kfac-jax) (📥 650 / month · 📦 2 · ⏱️ 20.05.2025): +- [PyPi](https://pypi.org/project/kfac-jax) (📥 1K / month · 📦 2 · ⏱️ 20.05.2025): ``` pip install kfac-jax ```
-
cuEquivariance (🥇20 · ⭐ 240) - cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used.. Apache-2 rep-learn +
cuEquivariance (🥇20 · ⭐ 250) - cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used.. Apache-2 rep-learn -- [GitHub](https://github.com/NVIDIA/cuEquivariance) (👨‍💻 5 · 🔀 15 · 📋 24 - 12% open · ⏱️ 19.06.2025): +- [GitHub](https://github.com/NVIDIA/cuEquivariance) (👨‍💻 5 · 🔀 17 · 📋 26 - 19% open · ⏱️ 19.06.2025): ``` git clone https://github.com/NVIDIA/cuEquivariance ``` -- [PyPi](https://pypi.org/project/cuequivariance) (📥 22K / month · 📦 6 · ⏱️ 19.06.2025): +- [PyPi](https://pypi.org/project/cuequivariance) (📥 26K / month · 📦 6 · ⏱️ 19.06.2025): ``` pip install cuequivariance ``` -- [Conda](https://anaconda.org/conda-forge/cuequivariance) (📥 6.4K · ⏱️ 10.06.2025): +- [Conda](https://anaconda.org/conda-forge/cuequivariance) (📥 6.8K · ⏱️ 10.06.2025): ``` conda install -c conda-forge cuequivariance ```
SpheriCart (🥈18 · ⭐ 86) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT -- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 12 · 🔀 15 · 📥 380 · 📦 7 · 📋 48 - 47% open · ⏱️ 20.05.2025): +- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 12 · 🔀 15 · 📥 390 · 📦 7 · 📋 48 - 47% open · ⏱️ 20.05.2025): ``` git clone https://github.com/lab-cosmo/sphericart ``` -- [PyPi](https://pypi.org/project/sphericart) (📥 2.1K / month · ⏱️ 28.04.2025): +- [PyPi](https://pypi.org/project/sphericart) (📥 2.4K / month · ⏱️ 28.04.2025): ``` pip install sphericart ```
-
Polynomials4ML.jl (🥈15 · ⭐ 13) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia +
OpenEquivariance (🥈15 · ⭐ 74) - OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product. BSD-3 rep-learn -- [GitHub](https://github.com/ACEsuit/Polynomials4ML.jl) (👨‍💻 12 · 🔀 6 · 📋 57 - 12% open · ⏱️ 23.06.2025): +- [GitHub](https://github.com/PASSIONLab/OpenEquivariance) (👨‍💻 3 · 🔀 6 · 📋 25 - 20% open · ⏱️ 03.07.2025): ``` - git clone https://github.com/ACEsuit/Polynomials4ML.jl + git clone https://github.com/PASSIONLab/OpenEquivariance ```
-
OpenEquivariance (🥈14 · ⭐ 72 · 📈) - OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product. BSD-3 rep-learn +
Polynomials4ML.jl (🥈15 · ⭐ 13) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia -- [GitHub](https://github.com/PASSIONLab/OpenEquivariance) (👨‍💻 3 · 🔀 6 · 📋 23 - 21% open · ⏱️ 22.06.2025): +- [GitHub](https://github.com/ACEsuit/Polynomials4ML.jl) (👨‍💻 12 · 🔀 6 · 📋 57 - 12% open · ⏱️ 23.06.2025): ``` - git clone https://github.com/PASSIONLab/OpenEquivariance + git clone https://github.com/ACEsuit/Polynomials4ML.jl ```
GElib (🥉12 · ⭐ 25) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0 C++ -- [GitHub](https://github.com/risi-kondor/GElib) (👨‍💻 6 · 🔀 3 · 📋 8 - 50% open · ⏱️ 15.06.2025): +- [GitHub](https://github.com/risi-kondor/GElib) (👨‍💻 6 · 🔀 3 · 📋 8 - 50% open · ⏱️ 03.07.2025): ``` git clone https://github.com/risi-kondor/GElib @@ -2039,7 +2021,7 @@ _Projects that implement mathematical objects used in atomistic machine learning
Show 7 hidden projects... - gpax (🥈17 · ⭐ 220 · 💀) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning -- lie-nn (🥉9 · ⭐ 32 · 💀) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn +- lie-nn (🥉9 · ⭐ 34 · 💀) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn - LapJAX (🥉8 · ⭐ 72 · 💀) - A JAX based package designed for efficient second order operators (e.g., laplacian) computation. MIT - EquivariantOperators.jl (🥉6 · ⭐ 19 · 💀) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT Julia - COSMO Toolbox (🥉6 · ⭐ 7 · 💀) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++ @@ -2061,14 +2043,14 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/jax-md/jax-md ``` -- [PyPi](https://pypi.org/project/jax-md) (📥 3.3K / month · 📦 3 · ⏱️ 09.08.2023): +- [PyPi](https://pypi.org/project/jax-md) (📥 4.8K / month · 📦 3 · ⏱️ 09.08.2023): ``` pip install jax-md ```
-
GPUMD (🥇21 · ⭐ 590) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 ML-IAP C++ electrostatics +
GPUMD (🥇21 · ⭐ 600) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 ML-IAP C++ electrostatics -- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 50 · 🔀 140 · 📋 230 - 7% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 50 · 🔀 140 · 📋 230 - 7% open · ⏱️ 02.07.2025): ``` git clone https://github.com/brucefan1983/GPUMD @@ -2081,7 +2063,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/luigibonati/mlcolvar ``` -- [PyPi](https://pypi.org/project/mlcolvar) (📥 470 / month · ⏱️ 19.02.2025): +- [PyPi](https://pypi.org/project/mlcolvar) (📥 400 / month · ⏱️ 19.02.2025): ``` pip install mlcolvar ``` @@ -2093,14 +2075,26 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/Radical-AI/torch-sim ``` -- [PyPi](https://pypi.org/project/torch-sim-atomistic) (📥 2.9K / month · ⏱️ 10.06.2025): +- [PyPi](https://pypi.org/project/torch-sim-atomistic) (📥 5K / month · ⏱️ 10.06.2025): ``` pip install torch-sim-atomistic ```
-
FitSNAP (🥈19 · ⭐ 170) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0 +
openmm-torch (🥈18 · ⭐ 200) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++ -- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 25 · 🔀 58 · 📥 15 · 📋 77 - 20% open · ⏱️ 27.05.2025): +- [GitHub](https://github.com/openmm/openmm-torch) (👨‍💻 9 · 🔀 29 · 📋 97 - 29% open · ⏱️ 20.02.2025): + + ``` + git clone https://github.com/openmm/openmm-torch + ``` +- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 890K · ⏱️ 20.06.2025): + ``` + conda install -c conda-forge openmm-torch + ``` +
+
FitSNAP (🥈18 · ⭐ 170) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0 + +- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 25 · 🔀 58 · 📥 15 · 📋 77 - 20% open · ⏱️ 03.07.2025): ``` git clone https://github.com/FitSNAP/FitSNAP @@ -2110,19 +2104,15 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach conda install -c conda-forge fitsnap3 ```
-
openmm-torch (🥈18 · ⭐ 200) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++ +
Psiflow (🥉14 · ⭐ 140) - scalable molecular simulation. MIT ML-IAP active-learning sampling -- [GitHub](https://github.com/openmm/openmm-torch) (👨‍💻 9 · 🔀 28 · 📋 97 - 29% open · ⏱️ 20.02.2025): +- [GitHub](https://github.com/molmod/psiflow) (👨‍💻 5 · 🔀 12 · 📋 56 - 21% open · ⏱️ 30.06.2025): ``` - git clone https://github.com/openmm/openmm-torch - ``` -- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 880K · ⏱️ 20.06.2025): - ``` - conda install -c conda-forge openmm-torch + git clone https://github.com/molmod/psiflow ```
-
OpenMM-ML (🥉15 · ⭐ 110) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP +
OpenMM-ML (🥉14 · ⭐ 110) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP - [GitHub](https://github.com/openmm/openmm-ml) (👨‍💻 5 · 🔀 26 · 📋 62 - 40% open · ⏱️ 12.03.2025): @@ -2134,15 +2124,15 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach conda install -c conda-forge openmm-ml ```
-
Psiflow (🥉14 · ⭐ 140) - scalable molecular simulation. MIT ML-IAP active-learning sampling +
pair_allegro (🥉13 · ⭐ 45) - LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials. MIT ML-IAP rep-learn -- [GitHub](https://github.com/molmod/psiflow) (👨‍💻 5 · 🔀 12 · 📋 56 - 23% open · ⏱️ 24.06.2025): +- [GitHub](https://github.com/mir-group/pair_nequip_allegro) (👨‍💻 4 · 🔀 8 · 📋 41 - 17% open · ⏱️ 16.05.2025): ``` - git clone https://github.com/molmod/psiflow + git clone https://github.com/mir-group/pair_allegro ```
-
DMFF (🥉13 · ⭐ 180) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0 C++ +
DMFF (🥉12 · ⭐ 180) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0 C++ - [GitHub](https://github.com/deepmodeling/DMFF) (👨‍💻 14 · 🔀 46 · 📋 28 - 39% open · ⏱️ 10.04.2025): @@ -2150,15 +2140,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach git clone https://github.com/deepmodeling/DMFF ```
-
pair_allegro (🥉13 · ⭐ 45) - LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials. MIT ML-IAP rep-learn - -- [GitHub](https://github.com/mir-group/pair_nequip_allegro) (👨‍💻 4 · 🔀 8 · 📋 41 - 17% open · ⏱️ 16.05.2025): - - ``` - git clone https://github.com/mir-group/pair_allegro - ``` -
-
pair_nequip (🥉11 · ⭐ 44) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn +
pair_nequip (🥉10 · ⭐ 44) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn - [GitHub](https://github.com/mir-group/pair_nequip) (👨‍💻 3 · 🔀 13 · 📋 33 - 39% open · ⏱️ 25.04.2025): @@ -2166,7 +2148,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach git clone https://github.com/mir-group/pair_nequip ```
-
PACE (🥉9 · ⭐ 29) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom +
PACE (🥉8 · ⭐ 29 · 💤) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom - [GitHub](https://github.com/ICAMS/lammps-user-pace) (👨‍💻 8 · 🔀 13 · 📋 8 - 25% open · ⏱️ 17.12.2024): @@ -2231,9 +2213,9 @@ _Projects that focus on reinforcement learning for atomistic ML._ _Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering._ -
cdk (🥇26 · ⭐ 540) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java +
cdk (🥇25 · ⭐ 540 · 📉) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java -- [GitHub](https://github.com/cdk/cdk) (👨‍💻 170 · 🔀 170 · 📥 27K · 📋 310 - 9% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/cdk/cdk) (👨‍💻 170 · 🔀 170 · 📥 27K · 📋 310 - 9% open · ⏱️ 27.06.2025): ``` git clone https://github.com/cdk/cdk @@ -2274,7 +2256,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp ``` git clone https://github.com/WMD-group/ElementEmbeddings ``` -- [PyPi](https://pypi.org/project/ElementEmbeddings) (📥 970 / month · ⏱️ 18.09.2024): +- [PyPi](https://pypi.org/project/ElementEmbeddings) (📥 1.2K / month · ⏱️ 18.09.2024): ``` pip install ElementEmbeddings ``` @@ -2285,20 +2267,20 @@ _Projects that offer implementations of representations aka descriptors, fingerp
Featomic (🥈15 · ⭐ 72) - Computing representations for atomistic machine learning. BSD-3 Rust C++ -- [GitHub](https://github.com/metatensor/featomic) (👨‍💻 16 · 🔀 15 · 📥 190 · 📋 83 - 50% open · ⏱️ 07.06.2025): +- [GitHub](https://github.com/metatensor/featomic) (👨‍💻 16 · 🔀 15 · 📥 200 · 📋 83 - 50% open · ⏱️ 07.06.2025): ``` git clone https://github.com/metatensor/featomic ```
-
pySIPFENN (🥈14 · ⭐ 24) - Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique.. LGPL-3.0 material-defect Defects & Disorder pretrained transfer-learning +
pySIPFENN (🥈13 · ⭐ 24) - Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique.. LGPL-3.0 material-defect Defects & Disorder pretrained transfer-learning - [GitHub](https://github.com/PhasesResearchLab/pySIPFENN) (👨‍💻 4 · 🔀 5 · 📥 110 · 📦 7 · 📋 6 - 66% open · ⏱️ 25.04.2025): ``` git clone https://github.com/PhasesResearchLab/pySIPFENN ``` -- [PyPi](https://pypi.org/project/pysipfenn) (📥 92 / month · ⏱️ 06.03.2025): +- [PyPi](https://pypi.org/project/pysipfenn) (📥 95 / month · ⏱️ 06.03.2025): ``` pip install pysipfenn ``` @@ -2309,20 +2291,20 @@ _Projects that offer implementations of representations aka descriptors, fingerp
SISSO (🥈12 · ⭐ 280) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran -- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 85 · 📋 77 - 23% open · ⏱️ 21.03.2025): +- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 86 · 📋 77 - 23% open · ⏱️ 21.03.2025): ``` git clone https://github.com/rouyang2017/SISSO ```
-
GlassPy (🥈12 · ⭐ 31 · 💤) - Python module for scientists working with glass materials. GPL-3.0 +
GlassPy (🥈12 · ⭐ 32 · 💤) - Python module for scientists working with glass materials. GPL-3.0 - [GitHub](https://github.com/drcassar/glasspy) (👨‍💻 2 · 🔀 7 · 📦 7 · 📋 15 - 46% open · ⏱️ 13.10.2024): ``` git clone https://github.com/drcassar/glasspy ``` -- [PyPi](https://pypi.org/project/glasspy) (📥 220 / month · ⏱️ 05.09.2024): +- [PyPi](https://pypi.org/project/glasspy) (📥 240 / month · ⏱️ 05.09.2024): ``` pip install glasspy ``` @@ -2339,7 +2321,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp pip install pdyna ```
-
MOLPIPx (🥉7 · ⭐ 37) - Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust. Apache-2 Python Rust +
MOLPIPx (🥉6 · ⭐ 37) - Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust. Apache-2 Python Rust - [GitHub](https://github.com/ChemAI-Lab/molpipx) (👨‍💻 10 · 🔀 1 · ⏱️ 14.04.2025): @@ -2347,20 +2329,20 @@ _Projects that offer implementations of representations aka descriptors, fingerp git clone https://github.com/ChemAI-Lab/molpipx ```
-
fplib (🥉7 · ⭐ 7) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper +
milad (🥉6 · ⭐ 32 · 💤) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative -- [GitHub](https://github.com/Rutgers-ZRG/libfp) (👨‍💻 2 · 🔀 1 · 📦 2 · ⏱️ 16.04.2025): +- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 2 · 📦 3 · ⏱️ 20.08.2024): ``` - git clone https://github.com/zhuligs/fplib + git clone https://github.com/muhrin/milad ```
-
milad (🥉6 · ⭐ 32 · 💤) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative +
fplib (🥉6 · ⭐ 7) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper -- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 2 · 📦 3 · ⏱️ 20.08.2024): +- [GitHub](https://github.com/Rutgers-ZRG/libfp) (👨‍💻 2 · 🔀 1 · 📦 2 · ⏱️ 16.04.2025): ``` - git clone https://github.com/muhrin/milad + git clone https://github.com/zhuligs/fplib ```
SA-GPR (🥉5 · ⭐ 20) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang @@ -2373,24 +2355,24 @@ _Projects that offer implementations of representations aka descriptors, fingerp
Show 18 hidden projects... -- DScribe (🥇24 · ⭐ 440 · 💀) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2 +- DScribe (🥇23 · ⭐ 440 · 💀) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2 - CatLearn (🥈15 · ⭐ 110 · 💀) - GPL-3.0 surface-science - Librascal (🥈13 · ⭐ 81 · 💀) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1 - CBFV (🥈12 · ⭐ 27 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed -- cmlkit (🥈11 · ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking +- cmlkit (🥈11 · ⭐ 33 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking - BenchML (🥈11 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking -- SkipAtom (🥉10 · ⭐ 26 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT +- SkipAtom (🥉9 · ⭐ 26 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT - ElemNet (🥉7 · ⭐ 95 · 💀) - Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction. Unlicensed single-paper - NICE (🥉7 · ⭐ 12 · 💀) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT - SISSO++ (🥉7 · ⭐ 3 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++ - SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++ - soap_turbo (🥉6 · ⭐ 7 · 💀) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran - pyLODE (🥉6 · ⭐ 3 · 💀) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics -- AMP (🥉6 · 💀) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed - MXenes4HER (🥉5 · ⭐ 6 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. GPL-3.0 materials-discovery catalysis scikit-learn single-paper +- AMP (🥉5 · 💀) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed - automl-materials (🥉4 · ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper -- magnetism-prediction (🥉4 · ⭐ 1) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper - ML-for-CurieTemp-Predictions (🥉3 · ⭐ 2 · 💀) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism +- magnetism-prediction (🥉3 · ⭐ 1) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper

@@ -2400,14 +2382,14 @@ _Projects that offer implementations of representations aka descriptors, fingerp _General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN)._ -
Deep Graph Library (DGL) (🥇36 · ⭐ 14K · 📉) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 +
Deep Graph Library (DGL) (🥇36 · ⭐ 14K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 - [GitHub](https://github.com/dmlc/dgl) (👨‍💻 300 · 🔀 3K · 📦 4.1K · 📋 2.9K - 18% open · ⏱️ 11.02.2025): ``` git clone https://github.com/dmlc/dgl ``` -- [PyPi](https://pypi.org/project/dgl) (📥 96K / month · 📦 150 · ⏱️ 13.05.2024): +- [PyPi](https://pypi.org/project/dgl) (📥 98K / month · 📦 150 · ⏱️ 13.05.2024): ``` pip install dgl ``` @@ -2418,7 +2400,7 @@ _General models that learn a representations aka embeddings of atomistic systems
PyG Models (🥇34 · ⭐ 23K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml -- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 550 · 🔀 3.8K · 📦 10K · 📋 3.9K - 30% open · ⏱️ 13.06.2025): +- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 550 · 🔀 3.8K · 📦 10K · 📋 3.9K - 30% open · ⏱️ 02.07.2025): ``` git clone https://github.com/pyg-team/pytorch_geometric @@ -2426,16 +2408,16 @@ _General models that learn a representations aka embeddings of atomistic systems
e3nn (🥇29 · ⭐ 1.1K) - A modular framework for neural networks with Euclidean symmetry. MIT -- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 36 · 🔀 160 · 📦 530 · 📋 170 - 17% open · ⏱️ 01.05.2025): +- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 36 · 🔀 160 · 📦 540 · 📋 170 - 17% open · ⏱️ 01.05.2025): ``` git clone https://github.com/e3nn/e3nn ``` -- [PyPi](https://pypi.org/project/e3nn) (📥 210K / month · 📦 46 · ⏱️ 22.03.2025): +- [PyPi](https://pypi.org/project/e3nn) (📥 170K / month · 📦 46 · ⏱️ 22.03.2025): ``` pip install e3nn ``` -- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 41K · ⏱️ 22.04.2025): +- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 42K · ⏱️ 22.04.2025): ``` conda install -c conda-forge e3nn ``` @@ -2447,35 +2429,35 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/atomistic-machine-learning/schnetpack ``` -- [PyPi](https://pypi.org/project/schnetpack) (📥 980 / month · 📦 4 · ⏱️ 05.09.2024): +- [PyPi](https://pypi.org/project/schnetpack) (📥 1.1K / month · 📦 4 · ⏱️ 05.09.2024): ``` pip install schnetpack ```
MatGL (Materials Graph Library) (🥇27 · ⭐ 360) - Graph deep learning library for materials. BSD-3 ML-IAP pretrained multifidelity -- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 21 · 🔀 77 · 📦 77 · 📋 120 - 5% open · ⏱️ 13.06.2025): +- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 21 · 🔀 77 · 📦 77 · 📋 120 - 5% open · ⏱️ 02.07.2025): ``` git clone https://github.com/materialsvirtuallab/matgl ``` -- [PyPi](https://pypi.org/project/matgl) (📥 20K / month · 📦 28 · ⏱️ 19.05.2025): +- [PyPi](https://pypi.org/project/matgl) (📥 18K / month · 📦 28 · ⏱️ 19.05.2025): ``` pip install matgl ``` -- [Docker Hub](https://hub.docker.com/r/materialsvirtuallab/matgl) (📥 91 · ⭐ 1 · ⏱️ 08.04.2025): +- [Docker Hub](https://hub.docker.com/r/materialsvirtuallab/matgl) (📥 92 · ⭐ 1 · ⏱️ 08.04.2025): ``` docker pull materialsvirtuallab/matgl ```
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ALIGNN (🥇20 · ⭐ 270) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ.. Custom +
ALIGNN (🥈19 · ⭐ 270) - This repository is no longer maintained. For the latest updates and continued development, please visit:.. Custom -- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 95 · 📦 23 · 📋 76 - 63% open · ⏱️ 02.04.2025): +- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 96 · 📦 23 · 📋 76 - 63% open · ⏱️ 27.06.2025): ``` git clone https://github.com/usnistgov/alignn ``` -- [PyPi](https://pypi.org/project/alignn) (📥 6.2K / month · 📦 11 · ⏱️ 02.04.2025): +- [PyPi](https://pypi.org/project/alignn) (📥 6.4K / month · 📦 11 · ⏱️ 02.04.2025): ``` pip install alignn ``` @@ -2487,24 +2469,24 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/e3nn/e3nn-jax ``` -- [PyPi](https://pypi.org/project/e3nn-jax) (📥 6.2K / month · 📦 13 · ⏱️ 14.08.2024): +- [PyPi](https://pypi.org/project/e3nn-jax) (📥 7.7K / month · 📦 13 · ⏱️ 14.08.2024): ``` pip install e3nn-jax ```
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kgcnn (🥈19 · ⭐ 120) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT +
kgcnn (🥈18 · ⭐ 120) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT - [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 31 · 📦 20 · 📋 87 - 14% open · ⏱️ 05.01.2025): ``` git clone https://github.com/aimat-lab/gcnn_keras ``` -- [PyPi](https://pypi.org/project/kgcnn) (📥 260 / month · 📦 3 · ⏱️ 08.01.2025): +- [PyPi](https://pypi.org/project/kgcnn) (📥 370 / month · 📦 3 · ⏱️ 08.01.2025): ``` pip install kgcnn ```
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Uni-Mol (🥈17 · ⭐ 890) - Official Repository for the Uni-Mol Series Methods. MIT pretrained +
Uni-Mol (🥈17 · ⭐ 900) - Official Repository for the Uni-Mol Series Methods. MIT pretrained - [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 20 · 🔀 140 · 📥 18K · 📋 210 - 47% open · ⏱️ 29.05.2025): @@ -2519,12 +2501,12 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/QUVA-Lab/escnn ``` -- [PyPi](https://pypi.org/project/escnn) (📥 3.3K / month · 📦 6 · ⏱️ 01.04.2022): +- [PyPi](https://pypi.org/project/escnn) (📥 3.5K / month · 📦 6 · ⏱️ 01.04.2022): ``` pip install escnn ```
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matsciml (🥈15 · ⭐ 170 · 📉) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking +
matsciml (🥈15 · ⭐ 180) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking - [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 14 · 🔀 27 · 📋 67 - 35% open · ⏱️ 24.03.2025): @@ -2532,15 +2514,15 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/IntelLabs/matsciml ```
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hippynn (🥈14 · ⭐ 81) - python library for atomistic machine learning. Custom workflows +
hippynn (🥈14 · ⭐ 83) - python library for atomistic machine learning. Custom workflows -- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 16 · 🔀 27 · 📦 2 · 📋 30 - 30% open · ⏱️ 17.06.2025): +- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 16 · 🔀 27 · 📦 2 · 📋 30 - 30% open · ⏱️ 27.06.2025): ``` git clone https://github.com/lanl/hippynn ```
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HydraGNN (🥈14 · ⭐ 81) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3 +
HydraGNN (🥈14 · ⭐ 83) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3 - [GitHub](https://github.com/ORNL/HydraGNN) (👨‍💻 16 · 🔀 30 · 📦 3 · 📋 54 - 31% open · ⏱️ 13.06.2025): @@ -2555,12 +2537,12 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/sparks-baird/CrabNet ``` -- [PyPi](https://pypi.org/project/crabnet) (📥 190 / month · 📦 2 · ⏱️ 10.01.2023): +- [PyPi](https://pypi.org/project/crabnet) (📥 220 / month · 📦 2 · ⏱️ 10.01.2023): ``` pip install crabnet ```
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GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 80) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT +
GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 80 · 💤) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT - [GitHub](https://github.com/superlouis/GATGNN) (👨‍💻 4 · 🔀 17 · 📋 7 - 57% open · ⏱️ 17.12.2024): @@ -2570,7 +2552,7 @@ _General models that learn a representations aka embeddings of atomistic systems
Equiformer (🥉8 · ⭐ 240) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer -- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 45 · 📋 21 - 47% open · ⏱️ 11.02.2025): +- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 46 · 📋 21 - 47% open · ⏱️ 11.02.2025): ``` git clone https://github.com/atomicarchitects/equiformer @@ -2584,7 +2566,7 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/llnl/graphite ```
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GNNOpt (🥉8 · ⭐ 31) - Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures. MIT optical-properties single-paper +
GNNOpt (🥉8 · ⭐ 31 · 💤) - Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures. MIT optical-properties single-paper - [GitHub](https://github.com/nguyen-group/GNNOpt) (🔀 8 · ⏱️ 19.12.2024): @@ -2600,6 +2582,14 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/Hongyu-yu/T-e3nn ```
+
Graph-Aware-Transformers (🥉7 · ⭐ 60 · 🐣) - Graph-Aware Attention for Adaptive Dynamics in Transformers. Apache-2 transformer graph-data pretrained single-paper + +- [GitHub](https://github.com/lamm-mit/Graph-Aware-Transformers) (👨‍💻 3 · 🔀 7 · ⏱️ 08.01.2025): + + ``` + git clone https://github.com/lamm-mit/Graph-Aware-Transformers + ``` +
PolyGNN (🥉7 · ⭐ 41) - polyGNN is a Python library to automate ML model training for polymer informatics. MIT soft-matter multitask single-paper - [GitHub](https://github.com/Ramprasad-Group/polygnn) (👨‍💻 4 · 🔀 9 · ⏱️ 05.02.2025): @@ -2616,15 +2606,7 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/Open-Catalyst-Project/AdsorbML ```
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Graph-Aware-Transformers (🥉6 · ⭐ 59 · 🐣) - Graph-Aware Attention for Adaptive Dynamics in Transformers. Apache-2 transformer graph-data pretrained single-paper - -- [GitHub](https://github.com/lamm-mit/Graph-Aware-Transformers) (👨‍💻 3 · 🔀 7 · ⏱️ 08.01.2025): - - ``` - git clone https://github.com/lamm-mit/Graph-Aware-Transformers - ``` -
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Crystalframer (🥉5 · ⭐ 8 · 🐣) - The official code respository for Rethinking the role of frames for SE(3)-invariant crystal structure modeling (ICLR.. MIT transformer single-paper +
Crystalframer (🥉6 · ⭐ 9 · 🐣) - The official code respository for Rethinking the role of frames for SE(3)-invariant crystal structure modeling (ICLR.. MIT transformer single-paper - [GitHub](https://github.com/omron-sinicx/crystalframer) (👨‍💻 2 · 🔀 1 · ⏱️ 03.05.2025): @@ -2652,19 +2634,19 @@ _General models that learn a representations aka embeddings of atomistic systems - UVVisML (🥉8 · ⭐ 32 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic - tensorfieldnetworks (🥉7 · ⭐ 160 · 💀) - Rotation- and translation-equivariant neural networks for 3D point clouds. MIT - DTNN (🥉7 · ⭐ 77 · 💀) - Deep Tensor Neural Network. MIT -- DeeperGATGNN (🥉7 · ⭐ 61 · 💀) - Scalable graph neural networks for materials property prediction. MIT +- DeeperGATGNN (🥉7 · ⭐ 62 · 💀) - Scalable graph neural networks for materials property prediction. MIT - Cormorant (🥉7 · ⭐ 60 · 💀) - Codebase for Cormorant Neural Networks. Custom - escnn_jax (🥉7 · ⭐ 30 · 💀) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom - CGAT (🥉7 · ⭐ 28 · 💀) - Crystal graph attention neural networks for materials prediction. MIT - Geom3D (🥉6 · ⭐ 120 · 💀) - Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023. MIT benchmarking single-paper -- MACE-Layer (🥉6 · ⭐ 39 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT +- MACE-Layer (🥉6 · ⭐ 40 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT - charge_transfer_nnp (🥉6 · ⭐ 35 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics - GLAMOUR (🥉6 · ⭐ 23 · 💀) - Graph Learning over Macromolecule Representations. MIT single-paper - ML4pXRDs (🥉6 · ⭐ 3 · 💀) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper - Autobahn (🥉5 · ⭐ 29 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT - FieldSchNet (🥉5 · ⭐ 19 · 💀) - Deep neural network for molecules in external fields. MIT - SCFNN (🥉5 · ⭐ 15 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper -- CraTENet (🥉5 · ⭐ 14 · 💀) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena +- CraTENet (🥉5 · ⭐ 15 · 💀) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena - EGraFFBench (🥉5 · ⭐ 11 · 💀) - Unlicensed single-paper benchmarking ML-IAP - Per-site PAiNN (🥉5 · ⭐ 2 · 💀) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pretrained single-paper - Per-Site CGCNN (🥉5 · ⭐ 1 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT pretrained single-paper @@ -2695,7 +2677,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` git clone https://github.com/deepmodeling/deepmd-kit ``` -- [PyPi](https://pypi.org/project/deepmd-kit) (📥 11K / month · 📦 11 · ⏱️ 11.06.2025): +- [PyPi](https://pypi.org/project/deepmd-kit) (📥 14K / month · 📦 11 · ⏱️ 11.06.2025): ``` pip install deepmd-kit ``` @@ -2703,7 +2685,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` conda install -c conda-forge deepmd-kit ``` -- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.7K · ⭐ 1 · ⏱️ 12.06.2025): +- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.8K · ⭐ 1 · ⏱️ 12.06.2025): ``` docker pull deepmodeling/deepmd-kit ``` @@ -2715,7 +2697,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` git clone https://github.com/deepmodeling/deepmd-kit ``` -- [PyPi](https://pypi.org/project/deepmd-kit) (📥 11K / month · 📦 11 · ⏱️ 11.06.2025): +- [PyPi](https://pypi.org/project/deepmd-kit) (📥 14K / month · 📦 11 · ⏱️ 11.06.2025): ``` pip install deepmd-kit ``` @@ -2723,115 +2705,115 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` conda install -c conda-forge deepmd-kit ``` -- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.7K · ⭐ 1 · ⏱️ 12.06.2025): +- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 3.8K · ⭐ 1 · ⏱️ 12.06.2025): ``` docker pull deepmodeling/deepmd-kit ```
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FAIRChem EquiformerV2 models (🥈29 · ⭐ 1.5K) - FAIRChem implementation of Equiformer V2 (eqV2) models. MIT pretrained UIP rep-learn catalysis +
FAIRChem EquiformerV2 models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Equiformer V2 (eqV2) models. MIT pretrained UIP rep-learn catalysis -- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 53 · 🔀 330 · 📋 390 - 6% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 54 · 🔀 350 · 📋 400 - 4% open · ⏱️ 02.07.2025): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 100K / month · 📦 10 · ⏱️ 03.06.2025): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 65K / month · 📦 12 · ⏱️ 01.07.2025): ``` pip install fairchem-core ```
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FAIRChem eSEN models (🥈29 · ⭐ 1.5K) - FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147. MIT pretrained UIP rep-learn catalysis +
FAIRChem eSEN models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147. MIT pretrained UIP rep-learn catalysis -- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 53 · 🔀 330 · 📋 390 - 6% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/facebookresearch/fairchem) (👨‍💻 54 · 🔀 350 · 📋 400 - 4% open · ⏱️ 02.07.2025): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 100K / month · 📦 10 · ⏱️ 03.06.2025): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 65K / month · 📦 12 · ⏱️ 01.07.2025): ``` pip install fairchem-core ```
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SevenNet (🥈23 · ⭐ 180) - SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular.. GPL-3.0 ML-IAP MD pretrained +
SevenNet (🥈23 · ⭐ 190) - SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular.. GPL-3.0 ML-IAP MD pretrained -- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 16 · 🔀 37 · 📥 3K · 📋 64 - 25% open · ⏱️ 21.06.2025): +- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 16 · 🔀 38 · 📥 3K · 📋 64 - 25% open · ⏱️ 21.06.2025): ``` git clone https://github.com/MDIL-SNU/SevenNet ``` -- [PyPi](https://pypi.org/project/sevenn) (📥 9K / month · 📦 14 · ⏱️ 20.05.2025): +- [PyPi](https://pypi.org/project/sevenn) (📥 9.4K / month · 📦 14 · ⏱️ 20.05.2025): ``` pip install sevenn ```
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Orb Models (🥈21 · ⭐ 450) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained +
Orb Models (🥈20 · ⭐ 450) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained - [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 11 · 🔀 61 · 📦 19 · 📋 47 - 6% open · ⏱️ 30.04.2025): ``` git clone https://github.com/orbital-materials/orb-models ``` -- [PyPi](https://pypi.org/project/orb-models) (📥 9.6K / month · 📦 12 · ⏱️ 30.04.2025): +- [PyPi](https://pypi.org/project/orb-models) (📥 11K / month · 📦 12 · ⏱️ 30.04.2025): ``` pip install orb-models ```
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CHGNet (🥈21 · ⭐ 310) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom ML-IAP MD pretrained electrostatics magnetism structure-relaxation +
CHGNet (🥈20 · ⭐ 310) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom ML-IAP MD pretrained electrostatics magnetism structure-relaxation - [GitHub](https://github.com/CederGroupHub/chgnet) (👨‍💻 11 · 🔀 81 · 📦 59 · 📋 72 - 4% open · ⏱️ 14.04.2025): ``` git clone https://github.com/CederGroupHub/chgnet ``` -- [PyPi](https://pypi.org/project/chgnet) (📥 18K / month · 📦 21 · ⏱️ 16.09.2024): +- [PyPi](https://pypi.org/project/chgnet) (📥 17K / month · 📦 21 · ⏱️ 16.09.2024): ``` pip install chgnet ```
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MACE-FOUNDATION models (🥉19 · ⭐ 700) - MACE foundation models (MP, OMAT, Matpes). MIT ML-IAP pretrained rep-learn MD +
MACE-FOUNDATION models (🥉18 · ⭐ 700) - MACE foundation models (MP, OMAT, Matpes). MIT ML-IAP pretrained rep-learn MD - [GitHub](https://github.com/ACEsuit/mace-foundations) (👨‍💻 2 · 🔀 270 · 📥 150K · 📋 18 - 22% open · ⏱️ 12.06.2025): ``` git clone https://github.com/ACEsuit/mace-foundations ``` -- [PyPi](https://pypi.org/project/mace-torch) (📥 29K / month · 📦 36 · ⏱️ 01.05.2025): +- [PyPi](https://pypi.org/project/mace-torch) (📥 27K / month · 📦 36 · ⏱️ 01.05.2025): ``` pip install mace-torch ```
MatterSim (🥉18 · ⭐ 420) - MatterSim: A deep learning atomistic model across elements, temperatures and pressures. MIT ML-IAP active-learning multimodal phase-transition pretrained -- [GitHub](https://github.com/microsoft/mattersim) (👨‍💻 17 · 🔀 53 · 📥 27 · 📋 30 - 40% open · ⏱️ 19.05.2025): +- [GitHub](https://github.com/microsoft/mattersim) (👨‍💻 17 · 🔀 57 · 📥 27 · 📋 30 - 40% open · ⏱️ 19.05.2025): ``` git clone https://github.com/microsoft/mattersim ``` -- [PyPi](https://pypi.org/project/mattersim) (📥 13K / month · 📦 2 · ⏱️ 21.02.2025): +- [PyPi](https://pypi.org/project/mattersim) (📥 14K / month · 📦 2 · ⏱️ 21.02.2025): ``` pip install mattersim ```
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M3GNet (🥉16 · ⭐ 290) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained +
M3GNet (🥉15 · ⭐ 290) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained - [GitHub](https://github.com/materialsvirtuallab/m3gnet) (👨‍💻 16 · 🔀 68 · 📋 35 - 42% open · ⏱️ 07.04.2025): ``` git clone https://github.com/materialsvirtuallab/m3gnet ``` -- [PyPi](https://pypi.org/project/m3gnet) (📥 730 / month · 📦 5 · ⏱️ 17.11.2022): +- [PyPi](https://pypi.org/project/m3gnet) (📥 710 / month · 📦 5 · ⏱️ 17.11.2022): ``` pip install m3gnet ```
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PET-MAD (🥉15 · ⭐ 63 · 🐣) - PET-MAD, a universal interatomic potential for advanced materials modeling. BSD-3 ML-IAP MD rep-learn transformer +
PET-MAD (🥉15 · ⭐ 64 · 🐣) - PET-MAD, a universal interatomic potential for advanced materials modeling. BSD-3 ML-IAP MD rep-learn transformer -- [GitHub](https://github.com/lab-cosmo/pet-mad) (👨‍💻 8 · 🔀 4 · 📦 4 · 📋 2 - 50% open · ⏱️ 26.06.2025): +- [GitHub](https://github.com/lab-cosmo/pet-mad) (👨‍💻 8 · 🔀 4 · 📦 4 · 📋 2 - 50% open · ⏱️ 30.06.2025): ``` git clone https://github.com/lab-cosmo/pet-mad ``` -- [PyPi](https://pypi.org/project/pet-mad) (📥 540 / month · 📦 3 · ⏱️ 26.06.2025): +- [PyPi](https://pypi.org/project/pet-mad) (📥 600 / month · 📦 3 · ⏱️ 26.06.2025): ``` pip install pet-mad ``` @@ -2848,7 +2830,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large git clone https://github.com/atomind-ai/mlip-arena ```
-
GRACE (🥉11 · ⭐ 59) - GRACE models and gracemaker (as implemented in TensorPotential package). Custom ML-IAP pretrained MD rep-learn rep-eng +
GRACE (🥉10 · ⭐ 61) - GRACE models and gracemaker (as implemented in TensorPotential package). Custom ML-IAP pretrained MD rep-learn rep-eng - [GitHub](https://github.com/ICAMS/grace-tensorpotential) (👨‍💻 3 · 🔀 4 · 📦 6 · 📋 6 - 50% open · ⏱️ 24.06.2025): @@ -2856,7 +2838,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large git clone https://github.com/ICAMS/grace-tensorpotential ```
-
CHIPS-FF (🥉8 · ⭐ 43) - Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093. Custom benchmarking structure-optimization MD materials-discovery transport-phenomena +
CHIPS-FF (🥉8 · ⭐ 45) - Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093. Custom benchmarking structure-optimization MD materials-discovery transport-phenomena - [GitHub](https://github.com/usnistgov/chipsff) (👨‍💻 3 · 🔀 5 · 📋 2 - 50% open · ⏱️ 06.02.2025): @@ -2864,27 +2846,27 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large git clone https://github.com/usnistgov/chipsff ```
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ffonons (🥉7 · ⭐ 21) - Phonons from ML force fields. MIT benchmarking density-of-states +
EScAIP (🥉6 · ⭐ 51) - [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential. MIT ML-IAP rep-learn transformer single-paper -- [GitHub](https://github.com/janosh/ffonons) (👨‍💻 2 · 🔀 2 · 📦 2 · ⏱️ 08.12.2024): +- [GitHub](https://github.com/ASK-Berkeley/EScAIP) (👨‍💻 2 · 🔀 5 · 📥 5 · 📋 6 - 66% open · ⏱️ 06.03.2025): ``` - git clone https://github.com/janosh/ffonons - ``` -- [PyPi](https://pypi.org/project/ffonons) (📥 15 / month · ⏱️ 10.01.2024): - ``` - pip install ffonons + git clone https://github.com/ASK-Berkeley/EScAIP ```
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EScAIP (🥉6 · ⭐ 51) - [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential. MIT ML-IAP rep-learn transformer single-paper +
ffonons (🥉6 · ⭐ 21 · 💤) - Phonons from ML force fields. MIT benchmarking density-of-states -- [GitHub](https://github.com/ASK-Berkeley/EScAIP) (👨‍💻 2 · 🔀 5 · 📥 5 · 📋 6 - 66% open · ⏱️ 06.03.2025): +- [GitHub](https://github.com/janosh/ffonons) (👨‍💻 2 · 🔀 2 · 📦 2 · ⏱️ 08.12.2024): ``` - git clone https://github.com/ASK-Berkeley/EScAIP + git clone https://github.com/janosh/ffonons + ``` +- [PyPi](https://pypi.org/project/ffonons) (📥 18 / month · ⏱️ 10.01.2024): + ``` + pip install ffonons ```
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Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 57 · 💤) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool +
Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 58 · 💤) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool - [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 2 · 🔀 7 · 📋 5 - 40% open · ⏱️ 22.10.2024): @@ -2907,7 +2889,7 @@ _Projects that focus on unsupervised, semi- or self-supervised learning for atom ``` git clone https://github.com/sissa-data-science/DADApy ``` -- [PyPi](https://pypi.org/project/dadapy) (📥 110 / month · ⏱️ 11.04.2025): +- [PyPi](https://pypi.org/project/dadapy) (📥 120 / month · ⏱️ 11.04.2025): ``` pip install dadapy ``` @@ -2919,21 +2901,14 @@ _Projects that focus on unsupervised, semi- or self-supervised learning for atom ``` git clone https://github.com/sparks-baird/mat_discover ``` -- [PyPi](https://pypi.org/project/mat_discover) (📥 300 / month · ⏱️ 23.06.2023): +- [PyPi](https://pypi.org/project/mat_discover) (📥 390 / month · ⏱️ 23.06.2023): ``` pip install mat_discover ```
-
ASAP (🥈11 · ⭐ 150 · 💤) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT - -- [GitHub](https://github.com/BingqingCheng/ASAP) (👨‍💻 6 · 🔀 29 · 📦 8 · 📋 26 - 26% open · ⏱️ 27.06.2024): - - ``` - git clone https://github.com/BingqingCheng/ASAP - ``` -
-
Show 7 hidden projects... +
Show 8 hidden projects... +- ASAP (🥈11 · ⭐ 150 · 💀) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT - pumml (🥈11 · ⭐ 36 · 💀) - Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to.. MIT materials-discovery - Sketchmap (🥉8 · ⭐ 46 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++ - paper-ml-robustness-material-property (🥉5 · ⭐ 4 · 💀) - A critical examination of robustness and generalizability of machine learning prediction of materials properties. BSD-3 datasets single-paper @@ -2957,7 +2932,7 @@ _Projects that focus on visualization (viz.) for atomistic ML._ ``` git clone https://github.com/janosh/pymatviz ``` -- [PyPi](https://pypi.org/project/pymatviz) (📥 12K / month · 📦 6 · ⏱️ 02.05.2025): +- [PyPi](https://pypi.org/project/pymatviz) (📥 11K / month · 📦 6 · ⏱️ 02.05.2025): ``` pip install pymatviz ``` @@ -2969,50 +2944,58 @@ _Projects that focus on visualization (viz.) for atomistic ML._ ``` git clone https://github.com/materialsproject/crystaltoolkit ``` -- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 1.4K / month · 📦 12 · ⏱️ 11.06.2025): +- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 1.5K / month · 📦 12 · ⏱️ 11.06.2025): ``` pip install crystal-toolkit ```
Chemiscope (🥈19 · ⭐ 150) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript -- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 25 · 🔀 40 · 📥 500 · 📦 6 · 📋 150 - 28% open · ⏱️ 07.06.2025): +- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 25 · 🔀 40 · 📥 510 · 📦 6 · 📋 150 - 28% open · ⏱️ 07.06.2025): ``` git clone https://github.com/lab-cosmo/chemiscope ``` -- [npm](https://www.npmjs.com/package/chemiscope) (📥 45 / month · 📦 3 · ⏱️ 15.03.2023): +- [npm](https://www.npmjs.com/package/chemiscope) (📥 110 / month · 📦 3 · ⏱️ 15.03.2023): ``` npm install chemiscope ```
Elementari (🥉18 · ⭐ 160) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. MIT JavaScript -- [GitHub](https://github.com/janosh/matterviz) (👨‍💻 2 · 🔀 16 · 📦 4 · 📋 7 - 14% open · ⏱️ 24.06.2025): +- [GitHub](https://github.com/janosh/matterviz) (👨‍💻 2 · 🔀 16 · 📦 4 · 📋 9 - 22% open · ⏱️ 30.06.2025): ``` git clone https://github.com/janosh/elementari ``` -- [npm](https://www.npmjs.com/package/elementari) (📥 670 / month · 📦 2 · ⏱️ 19.06.2025): +- [npm](https://www.npmjs.com/package/elementari) (📥 690 / month · 📦 2 · ⏱️ 19.06.2025): ``` npm install elementari ```
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ZnDraw (🥉18 · ⭐ 42) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript +
ZnDraw (🥉18 · ⭐ 43) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript - [GitHub](https://github.com/zincware/ZnDraw) (👨‍💻 7 · 🔀 4 · 📦 11 · 📋 360 - 27% open · ⏱️ 18.02.2025): ``` git clone https://github.com/zincware/ZnDraw ``` -- [PyPi](https://pypi.org/project/zndraw) (📥 730 / month · 📦 5 · ⏱️ 19.02.2025): +- [PyPi](https://pypi.org/project/zndraw) (📥 770 / month · 📦 5 · ⏱️ 19.02.2025): ``` pip install zndraw ```
-
Show 1 hidden projects... +
Atomvision (🥉10 · ⭐ 35) - Deep learning framework for atomistic image data. Custom computer-vision experimental-data rep-learn -- Atomvision (🥉11 · ⭐ 35 · 💀) - Deep learning framework for atomistic image data. Custom computer-vision experimental-data rep-learn +- [GitHub](https://github.com/usnistgov/atomvision) (👨‍💻 3 · 🔀 17 · 📋 8 - 50% open · ⏱️ 27.06.2025): + + ``` + git clone https://github.com/usnistgov/atomvision + ``` +- [PyPi](https://pypi.org/project/atomvision) (📥 66 / month · ⏱️ 08.05.2023): + ``` + pip install atomvision + ```

@@ -3022,19 +3005,19 @@ _Projects that focus on visualization (viz.) for atomistic ML._ _Projects and models that focus on quantities of wavefunction theory methods, such as Monte Carlo techniques like deep learning variational Monte Carlo (DL-VMC), quantum chemistry methods, etc._ -
DeepQMC (🥇18 · ⭐ 380) - Deep learning quantum Monte Carlo for electrons in real space. MIT +
DeepQMC (🥇19 · ⭐ 380) - Deep learning quantum Monte Carlo for electrons in real space. MIT -- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 64 · 📦 3 · 📋 54 - 5% open · ⏱️ 28.05.2025): +- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 64 · 📦 3 · 📋 56 - 5% open · ⏱️ 28.05.2025): ``` git clone https://github.com/deepqmc/deepqmc ``` -- [PyPi](https://pypi.org/project/deepqmc) (📥 83 / month · ⏱️ 24.09.2024): +- [PyPi](https://pypi.org/project/deepqmc) (📥 200 / month · ⏱️ 24.09.2024): ``` pip install deepqmc ```
-
FermiNet (🥈15 · ⭐ 770) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer +
FermiNet (🥈16 · ⭐ 770) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer - [GitHub](https://github.com/google-deepmind/ferminet) (👨‍💻 22 · 🔀 150 · 📋 68 - 4% open · ⏱️ 02.06.2025): @@ -3050,19 +3033,19 @@ _Projects and models that focus on quantities of wavefunction theory methods, su git clone https://github.com/bytedance/jaqmc ```
-
DeepErwin (🥈8 · ⭐ 56) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom +
DeepErwin (🥉7 · ⭐ 57) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom -- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 9 · 🔀 8 · 📥 15 · 📦 2 · ⏱️ 18.04.2025): +- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 9 · 🔀 9 · 📥 15 · 📦 2 · ⏱️ 18.04.2025): ``` git clone https://github.com/mdsunivie/deeperwin ``` -- [PyPi](https://pypi.org/project/deeperwin) (📥 21 / month · ⏱️ 14.12.2021): +- [PyPi](https://pypi.org/project/deeperwin) (📥 29 / month · ⏱️ 14.12.2021): ``` pip install deeperwin ```
-
LapNet (🥉5 · ⭐ 64) - Efficient and Accurate Neural-Network Ansatz for Quantum Monte Carlo. Apache-2 +
LapNet (🥉5 · ⭐ 64 · 💤) - Efficient and Accurate Neural-Network Ansatz for Quantum Monte Carlo. Apache-2 - [GitHub](https://github.com/bytedance/LapNet) (👨‍💻 4 · 🔀 12 · ⏱️ 04.12.2024): @@ -3073,7 +3056,7 @@ _Projects and models that focus on quantities of wavefunction theory methods, su
Show 2 hidden projects... - ACEpsi.jl (🥉7 · ⭐ 2 · 💀) - ACE wave function parameterizations. MIT rep-eng Julia -- SchNOrb (🥉6 · ⭐ 64 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT +- SchNOrb (🥉6 · ⭐ 65 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT

diff --git a/history/2025-07-03_changes.md b/history/2025-07-03_changes.md new file mode 100644 index 0000000..92951bf --- /dev/null +++ b/history/2025-07-03_changes.md @@ -0,0 +1,20 @@ +## 📈 Trending Up + +_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._ + +- NequIP (🥇32 · ⭐ 740 · 📈) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- FAIR Chemistry datasets (🥇30 · ⭐ 1.6K · 📈) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +- fairchem (🥇30 · ⭐ 1.6K · 📈) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +- FAIRChem EquiformerV2 models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Equiformer V2 (eqV2) models. MIT pretrained UIP rep-learn catalysis +- FAIRChem eSEN models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147. MIT pretrained UIP rep-learn catalysis + +## 📉 Trending Down + +_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._ + +- RDKit (🥇37 · ⭐ 3K · 📉) - BSD-3 C++ cheminformatics +- JAX-DFT (🥇25 · ⭐ 36K · 📉) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 +- cdk (🥇25 · ⭐ 540 · 📉) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java +- TorchANI (🥇23 · ⭐ 500 · 💀) - Accurate Neural Network Potential on PyTorch. MIT +- SMACT (🥇23 · ⭐ 110 · 📉) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics + diff --git a/history/2025-07-03_projects.csv b/history/2025-07-03_projects.csv new file mode 100644 index 0000000..4f66d57 --- /dev/null +++ b/history/2025-07-03_projects.csv @@ -0,0 +1,515 @@ +,name,resource,category,homepage,description,projectrank,show,license,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_latest_release_published_at,pypi_dependent_project_count,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,trending,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,updated_github_id,maven_id,maven_url,maven_latest_release_published_at,maven_dependent_project_count,npm_id,npm_url,npm_latest_release_published_at,npm_dependent_project_count,npm_monthly_downloads,gitlab_id,gitlab_url,ignore,docs_url +0,ACE / GRACE support,True,community,https://acesupport.zulipchat.com,Support forum for the Atomic Cluster Expansion (ACE) and extensions.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +1,AI for Science Map,True,community,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,GPL-3.0 license,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +2,ASE ecosystem,True,community,https://wiki.fysik.dtu.dk/ase/ecosystem.html,This is a list of software packages related to ASE or using ASE.,0,True,,"['md, ml-iap']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +3,Atomic Cluster Expansion,True,community,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +4,CrystaLLM,True,community,https://crystallm.com,Generate a crystal structure from a composition.,0,True,https://materialis.ai/terms.html,"['language-models', 'generative', 'pretrained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +5,GAP-ML.org community homepage,True,community,https://gap-ml.org/,,0,True,,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +6,matsci.org,True,community,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +7,Matter Modeling Stack Exchange - Machine Learning,True,community,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +8,Alexandria Materials Database,True,datasets,https://alexandria.icams.rub.de/,"A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..",0,True,CC-BY-4.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +9,Catalysis Hub,True,datasets,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +10,Citrination Datasets,True,datasets,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,MIT,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +11,crystals.ai,True,datasets,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +12,DeepChem Models,True,datasets,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,,"['model-repository', 'pretrained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +13,Graphs of Materials Project 20190401,True,datasets,https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992,The dataset used to train the MEGNet interatomic potential.,0,True,MIT,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +14,HME21 Dataset,True,datasets,https://doi.org/10.6084/m9.figshare.19658538,High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP)..,0,True,,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +15,JARVIS-Leaderboard,True,datasets,https://pages.nist.gov/jarvis_leaderboard/,A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w.,0,True,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,"['model-repository', 'benchmarking', 'community', 'educational']",usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2025-06-27 04:36:22.000000,2025-06-27 04:36:22,924.0,1.0,47.0,3.0,353.0,18.0,2.0,69.0,2024-11-24 16:35:14.000,2024.10.30,30.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +16,Materials Project - Charge Densities,True,datasets,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +17,Materials Project Trajectory (MPtrj) Dataset,True,datasets,https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842,The dataset used to train the CHGNet universal potential.,0,True,MIT,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +18,matterverse.ai,True,datasets,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +19,MPF.2021.2.8,True,datasets,https://figshare.com/articles/dataset/MPF_2021_2_8/19470599,The dataset used to train the M3GNet universal potential.,0,True,,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +20,NRELMatDB,True,datasets,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +21,QM9 Charge Densities and Energies,True,datasets,https://data.dtu.dk/articles/dataset/QM9_Charge_Densities_and_Energies_Calculated_with_VASP/16794500,QM9 molecules calculated with VASP using Atomic Simulation Environment.,0,True,CC-BY-4.0,['ml-dft'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +22,QM40 Dataset,True,datasets,https://doi.org/10.6084/m9.figshare.25993060.v1,A More Realistic QM Dataset for Machine Learning in Molecular Science https://doi.org/10.1038/s41597-024-04206-y.,0,True,CC-BY-4.0,['drug-discovery'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +23,QMugs dataset,True,datasets,CC-BY-3.0,Quantum Mechanical Properties of Drug-like Molecules https://doi.org/10.1038/s41597-022-01390-7.,0,True,CC-BY-3.0,['drug-discovery'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +24,Quantum-Machine.org Datasets,True,datasets,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +25,sGDML Datasets,True,datasets,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +26,MoleculeNet,True,datasets,https://moleculenet.org/,A Benchmark for Molecular Machine Learning.,0,True,,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +27,ZINC15,True,datasets,https://zinc15.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +28,ZINC20,True,datasets,https://zinc.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +29,AI for Science 101,True,educational,https://ai4science101.github.io/,,0,True,,"['community', 'rep-learn']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +30,AL4MS 2023 workshop tutorials,True,educational,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +31,Quantum Chemistry in the Age of Machine Learning,True,educational,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +32,IKS-PIML,True,ml-dft,https://rodare.hzdr.de/record/2720,Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning..,0,True,,"['neural-operator', 'pinn', 'datasets', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +33,TeaNet,True,uip,https://doi.org/10.24433/CO.0749085.v1,Universal neural network interatomic potential inspired by iterative electronic relaxations..,0,True,,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +34,PreFerred Potential (PFP),True,uip,https://www.nature.com/articles/s41467-022-30687-9#code-availability,Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9.,0,True,,"['ml-iap', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +35,MaCBench Leaderboard,True,language-models,https://huggingface.co/spaces/jablonkagroup/MaCBench-Leaderboard,Leaderboard for multimodal language models for chemistry & materials research.,0,True,,"['community', 'benchmarking', 'datasets']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +36,M-OFDFT,True,ml-dft,https://zenodo.org/records/10616893,Overcoming the Barrier of Orbital-Free Density Functional Theory in Molecular Systems Using Deep Learning..,0,True,,"['transformer', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +37,RDKit,,general-tool,https://github.com/rdkit/rdkit,,37,True,BSD-3-Clause,"['lang-cpp', 'cheminformatics']",rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2025-07-03 11:22:28.000000,2025-07-03 11:22:28,8174.0,91.0,897.0,77.0,3597.0,668.0,3382.0,2983.0,2025-06-30 11:18:36.000,Release_2025_03_4,100.0,252.0,rdkit,rdkit/rdkit,1089.0,3.0,https://pypi.org/project/rdkit,2025-06-12 07:26:06.000,1086.0,1028784.0,1048283.0,https://anaconda.org/rdkit/rdkit,2025-03-25 16:26:20.859,2593443.0,1.0,-1.0,,,,,,,,,,,,,,,,,,,, +38,Deep Graph Library (DGL),,rep-learn,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",36,True,Apache-2.0,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2025-03-25 16:26:45.757000,2025-02-11 00:26:08,4416.0,,3018.0,175.0,5073.0,539.0,2361.0,13950.0,2024-09-03 04:16:25.000,2.4.0,453.0,296.0,dgl,dglteam/dgl,4246.0,4098.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,148.0,97603.0,103132.0,https://anaconda.org/dglteam/dgl,2025-03-25 16:26:45.757,436845.0,1.0,,,,,,,,,,,,,,,,,,,,, +39,PyG Models,,rep-learn,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,34,True,MIT,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2025-07-03 16:46:42.000000,2025-07-02 18:43:42,7836.0,74.0,3840.0,254.0,3527.0,1194.0,2716.0,22547.0,2024-09-26 07:09:50.000,2.6.1,42.0,552.0,,,10086.0,10086.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +40,DeepChem,,general-tool,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",34,True,MIT,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2025-07-02 18:34:40.002994,2025-07-02 17:28:11,10609.0,16.0,1881.0,142.0,2693.0,795.0,1247.0,6085.0,2024-04-03 16:21:23.000,2.8.0,997.0,258.0,deepchem,conda-forge/deepchem,656.0,636.0,https://pypi.org/project/deepchem,2025-07-02 18:15:39.000,20.0,40255.0,42245.0,https://anaconda.org/conda-forge/deepchem,2025-04-22 14:57:21.461,116918.0,1.0,,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2025-07-02 18:34:40.002994,5.0,8829.0,,,,,,,,,,,,,,, +41,NequIP,,ml-iap,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,32,True,MIT,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2025-07-01 18:45:23.000000,2025-07-01 14:42:38,2785.0,906.0,161.0,21.0,183.0,7.0,99.0,744.0,2025-07-01 02:53:06.000,0.12.0,26.0,21.0,nequip,conda-forge/nequip,52.0,39.0,https://pypi.org/project/nequip,2025-07-01 02:53:06.000,13.0,60972.0,61267.0,https://anaconda.org/conda-forge/nequip,2025-07-01 09:34:19.992,11239.0,1.0,1.0,,,,,,,,,,,,,,,,,,,, +42,DeePMD-kit,,ml-iap,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,30,True,LGPL-3.0,"['md', 'workflows', 'lang-cpp']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2025-07-03 03:03:41.000000,2025-06-11 05:53:58,3314.0,57.0,551.0,46.0,2435.0,99.0,812.0,1697.0,2025-06-11 06:08:21.000,3.1.0,62.0,79.0,deepmd-kit,deepmodeling/deepmd-kit,45.0,34.0,https://pypi.org/project/deepmd-kit,2025-06-11 06:08:21.000,11.0,14224.0,15113.0,https://anaconda.org/deepmodeling/deepmd-kit,2025-03-25 16:24:57.998,2502.0,1.0,,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2025-06-12 14:06:06.712335,1.0,3773.0,55221.0,,,,,,,,,,,,,, +43,DPA-2,,uip,https://github.com/deepmodeling/deepmd-kit,A large atomic model as a multi-task learner https://arxiv.org/abs/2312.15492.,30,True,LGPL-3.0,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2025-07-03 03:03:41.000000,2025-06-11 05:53:58,3314.0,57.0,551.0,46.0,2435.0,99.0,812.0,1697.0,2025-06-11 06:08:21.000,3.1.0,62.0,79.0,deepmd-kit,conda-forge/deepmd-kit,45.0,34.0,https://pypi.org/project/deepmd-kit,2025-06-11 06:08:21.000,11.0,14224.0,53968.0,https://anaconda.org/conda-forge/deepmd-kit,2025-06-11 11:42:47.882,1983464.0,1.0,,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2025-06-12 14:06:06.712335,1.0,3773.0,55221.0,,,,,,,,,,,,,, +44,DeePMD-DPA3,,uip,https://github.com/deepmodeling/deepmd-kit,Successor of DPA-2.,30,True,LGPL-3.0,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2025-07-03 03:03:41.000000,2025-06-11 05:53:58,3314.0,57.0,551.0,46.0,2435.0,99.0,812.0,1697.0,2025-06-11 06:08:21.000,3.1.0,62.0,79.0,deepmd-kit,conda-forge/deepmd-kit,45.0,34.0,https://pypi.org/project/deepmd-kit,2025-06-11 06:08:21.000,11.0,14224.0,53968.0,https://anaconda.org/conda-forge/deepmd-kit,2025-06-11 11:42:47.882,1983464.0,1.0,,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2025-06-12 14:06:06.712335,1.0,3773.0,55221.0,,,,,,,,,,,,,, +45,FAIR Chemistry datasets,,datasets,https://github.com/facebookresearch/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",30,True,MIT,['catalysis'],FAIR-Chem/fairchem,https://github.com/facebookresearch/fairchem,2019-09-26 04:47:27,2025-07-02 17:03:33.000000,2025-07-02 00:12:12,1048.0,74.0,346.0,33.0,919.0,19.0,379.0,1577.0,2025-07-01 20:07:32.000,2.3.0,32.0,54.0,fairchem-core,,12.0,,https://pypi.org/project/fairchem-core,2025-07-01 20:07:32.000,12.0,65137.0,65137.0,,,,1.0,1.0,,,,,,,facebookresearch/fairchem,,,,,,,,,,,,, +46,fairchem,,ml-iap,https://github.com/facebookresearch/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project.,30,True,MIT,"['pretrained', 'uip', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/facebookresearch/fairchem,2019-09-26 04:47:27,2025-07-02 17:03:33.000000,2025-07-02 00:12:12,1048.0,74.0,346.0,33.0,919.0,19.0,379.0,1577.0,2025-07-01 20:07:32.000,2.3.0,32.0,54.0,fairchem-core,,12.0,,https://pypi.org/project/fairchem-core,2025-07-01 20:07:32.000,12.0,65137.0,65137.0,,,,1.0,1.0,,,,,,,facebookresearch/fairchem,,,,,,,,,,,,, +47,FAIRChem EquiformerV2 models,,uip,https://github.com/FAIR-Chem/fairchem/tree/main/src/fairchem/core/models/equiformer_v2,FAIRChem implementation of Equiformer V2 (eqV2) models.,30,True,MIT,"['pretrained', 'uip', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/facebookresearch/fairchem,2019-09-26 04:47:27,2025-07-02 17:03:33.000000,2025-07-02 00:12:12,1048.0,74.0,346.0,33.0,919.0,19.0,379.0,1577.0,2025-07-01 20:07:32.000,2.3.0,32.0,54.0,fairchem-core,,12.0,,https://pypi.org/project/fairchem-core,2025-07-01 20:07:32.000,12.0,65137.0,65137.0,,,,1.0,1.0,,,,,,,facebookresearch/fairchem,,,,,,,,,,,,, +48,FAIRChem eSEN models,,uip,https://github.com/janosh/matbench-discovery/tree/main/models/eSEN,FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147.,30,True,MIT,"['pretrained', 'uip', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/facebookresearch/fairchem,2019-09-26 04:47:27,2025-07-02 17:03:33.000000,2025-07-02 00:12:12,1048.0,74.0,346.0,33.0,919.0,19.0,379.0,1577.0,2025-07-01 20:07:32.000,2.3.0,32.0,54.0,fairchem-core,,12.0,,https://pypi.org/project/fairchem-core,2025-07-01 20:07:32.000,12.0,65137.0,65137.0,,,,1.0,1.0,,,,,,,facebookresearch/fairchem,,,,,,,,,,,,, +49,Meta Open Materials 2024 (OMat24) Dataset,,datasets,https://huggingface.co/datasets/fairchem/OMAT24,Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity.,29,True,CC-BY-4.0,,FAIR-Chem/fairchem,https://github.com/facebookresearch/fairchem,2019-09-26 04:47:27,2025-07-02 17:03:33.000000,2025-07-02 00:12:12,1048.0,74.0,346.0,33.0,919.0,19.0,379.0,1577.0,2025-07-01 20:07:32.000,2.3.0,32.0,54.0,fairchem-core,,12.0,,https://pypi.org/project/fairchem-core,2025-07-01 20:07:32.000,12.0,65137.0,65137.0,,,,1.0,,,,,,,,facebookresearch/fairchem,,,,,,,,,,,,, +50,e3nn,,rep-learn,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,29,True,MIT,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2025-05-01 00:11:56.000000,2025-05-01 00:11:56,2197.0,3.0,157.0,18.0,252.0,31.0,142.0,1104.0,2025-03-22 20:33:48.000,0.5.6,33.0,36.0,e3nn,conda-forge/e3nn,581.0,535.0,https://pypi.org/project/e3nn,2025-03-22 20:33:48.000,46.0,167282.0,168391.0,https://anaconda.org/conda-forge/e3nn,2025-04-22 14:58:17.855,42174.0,1.0,,,,,,,,,,,,,,,,,,,,, +51,SchNetPack,,rep-learn,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,27,True,MIT,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2025-07-02 23:05:02.000000,2025-06-24 16:11:08,1815.0,50.0,224.0,29.0,470.0,6.0,271.0,851.0,2024-09-05 11:35:29.000,2.1.1,12.0,40.0,schnetpack,,110.0,106.0,https://pypi.org/project/schnetpack,2024-09-05 11:35:29.000,4.0,1076.0,1076.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +52,Matminer,,general-tool,https://github.com/hackingmaterials/matminer,Data mining for materials science.,27,True,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2025-06-30 08:06:54.000000,2024-10-11 14:20:38,4174.0,,197.0,25.0,728.0,31.0,200.0,532.0,2024-10-06 12:46:05.000,0.9.3,72.0,56.0,matminer,conda-forge/matminer,484.0,424.0,https://pypi.org/project/matminer,2024-10-06 12:46:05.000,60.0,31126.0,32752.0,https://anaconda.org/conda-forge/matminer,2025-04-22 14:57:14.656,92712.0,1.0,,,,,,,,,,,,,,,,,,,,, +53,MatGL (Materials Graph Library),,rep-learn,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,27,True,BSD-3-Clause,"['ml-iap', 'pretrained', 'multifidelity']",materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2025-07-02 21:52:51.000000,2025-07-02 21:52:50,1408.0,77.0,77.0,10.0,462.0,7.0,118.0,361.0,2025-05-18 19:21:09.000,1.2.7,38.0,21.0,matgl,,105.0,77.0,https://pypi.org/project/matgl,2025-05-19 16:06:48.000,28.0,17953.0,17955.0,,,,1.0,,materialsvirtuallab/matgl,https://hub.docker.com/r/materialsvirtuallab/matgl,2025-04-08 02:41:39.642894,1.0,92.0,,,,,,,,,,,,,,, +54,JAX-DFT,,ml-dft,https://github.com/google-research/google-research/tree/master/jax_dft,This library provides basic building blocks that can construct DFT calculations as a differentiable program.,25,True,Apache-2.0,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2025-06-30 21:25:22.000000,2025-06-30 21:25:14,4825.0,42.0,8116.0,754.0,929.0,1627.0,339.0,35912.0,,,,839.0,,,,,,,,,,,,,1.0,-1.0,,,,,,,,,,,,,,,,,,,, +55,cdk,,rep-eng,https://github.com/cdk/cdk,The Chemistry Development Kit.,25,True,LGPL-2.1,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2025-06-27 06:56:41.000000,2025-06-27 06:56:41,17992.0,7.0,167.0,41.0,884.0,29.0,282.0,538.0,2025-03-29 08:49:54.000,cdk-2.11,22.0,167.0,,,18.0,,,,,,204.0,,,,1.0,-1.0,,,,,,26586.0,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2025-03-29 08:42:06,18.0,,,,,,,,, +56,dgl-lifesci,,rep-learn,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,24,False,Apache-2.0,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000000,2023-04-16 03:55:52,236.0,,158.0,15.0,141.0,27.0,57.0,759.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,348.0,344.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,12954.0,12954.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +57,QUIP,,general-tool,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,24,True,GPL-2.0,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2025-05-13 15:52:55.000000,2025-04-22 20:48:10,10867.0,1.0,124.0,24.0,179.0,112.0,369.0,367.0,2023-06-15 19:54:01.129,0.9.14,15.0,86.0,quippy-ase,,50.0,46.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,3108.0,3188.0,,,,2.0,,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,10172.0,757.0,,,,,,,,,,,,,, +58,OPTIMADE Python tools,,datasets,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,24,True,MIT,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2025-06-30 06:38:47.000000,2025-06-10 12:04:41,1764.0,11.0,46.0,5.0,1822.0,102.0,371.0,76.0,2025-03-21 17:44:04.000,1.2.4,122.0,31.0,optimade,conda-forge/optimade,4.0,,https://pypi.org/project/optimade,2025-03-21 17:44:04.000,4.0,10543.0,12966.0,https://anaconda.org/conda-forge/optimade,2025-04-22 14:57:40.463,135728.0,1.0,,,,,,,,,,,,,,,,,,,,, +59,MPContribs,,datasets,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,24,True,MIT,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2025-06-30 08:12:26.000000,2025-06-11 23:57:56,5794.0,31.0,24.0,9.0,1823.0,29.0,80.0,38.0,2025-02-28 23:06:41.000,5.10.2,166.0,27.0,mpcontribs-client,,56.0,53.0,https://pypi.org/project/mpcontribs-client,2025-02-28 23:06:41.000,3.0,3010.0,3010.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +60,JAX-MD,,md,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",23,True,Apache-2.0,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-11-26 06:35:02.000000,2024-11-26 06:35:02,929.0,,209.0,46.0,177.0,86.0,83.0,1273.0,2024-11-26 06:32:07.000,jax-md-v0.2.25,38.0,39.0,jax-md,,75.0,72.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,4814.0,4814.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +61,MACE,,ml-iap,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,23,True,MIT,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2025-07-02 19:11:03.000000,2025-06-20 11:19:04,1408.0,107.0,279.0,24.0,368.0,97.0,357.0,769.0,2025-04-30 21:30:57.000,0.3.13,14.0,59.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +62,TorchANI,,ml-iap,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,23,False,MIT,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2025-04-22 14:57:33.391000,2023-11-14 16:32:59,434.0,,136.0,29.0,485.0,27.0,150.0,499.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,68.0,64.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,3708.0,19438.0,https://anaconda.org/conda-forge/torchani,2025-04-22 14:57:33.391,928092.0,1.0,-1.0,,,,,,,,,,,,,,,,,,,, +63,DScribe,,rep-eng,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,23,False,Apache-2.0,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2025-04-22 14:57:10.921000,2024-05-28 18:24:28,1288.0,,91.0,17.0,27.0,11.0,95.0,438.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,290.0,255.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,16762.0,20424.0,https://anaconda.org/conda-forge/dscribe,2025-04-22 14:57:10.921,219777.0,1.0,,,,,,,,,,,,,,,,,,,,, +64,MAML,,general-tool,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",23,True,BSD-3-Clause,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2025-06-02 20:08:01.000000,2025-06-02 20:08:00,1867.0,29.0,86.0,19.0,625.0,12.0,64.0,416.0,2025-04-02 04:02:59.000,2025.4.2,20.0,39.0,maml,,19.0,16.0,https://pypi.org/project/maml,2025-04-02 04:37:00.000,3.0,425.0,425.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +65,DP-GEN,,active-learning,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,23,True,LGPL-3.0,"['ml-iap', 'md', 'workflows']",deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2025-07-03 00:08:03.000000,2025-02-21 06:05:03,2168.0,,176.0,10.0,895.0,53.0,277.0,347.0,2025-02-21 06:06:33.000,0.13.1,20.0,69.0,dpgen,deepmodeling/dpgen,10.0,8.0,https://pypi.org/project/dpgen,2025-02-21 06:06:33.000,2.0,588.0,621.0,https://anaconda.org/deepmodeling/dpgen,2025-03-25 16:24:58.041,236.0,1.0,,,,,,,1959.0,,,,,,,,,,,,,, +66,pymatviz,,visualization,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,23,True,MIT,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2025-06-17 20:20:03.000000,2025-06-17 20:20:01,469.0,25.0,27.0,6.0,248.0,7.0,50.0,235.0,2025-05-02 12:29:22.000,0.16.0,36.0,11.0,pymatviz,,30.0,24.0,https://pypi.org/project/pymatviz,2025-05-02 12:29:22.000,6.0,11166.0,11166.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +67,dpdata,,data-structures,https://github.com/deepmodeling/dpdata,A Python package for manipulating atomistic data of software in computational science.,23,True,LGPL-3.0,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2025-07-03 00:07:46.000000,2025-03-20 03:30:40,813.0,,138.0,7.0,575.0,35.0,93.0,211.0,2025-03-20 03:32:07.000,0.2.24,51.0,62.0,dpdata,deepmodeling/dpdata,183.0,143.0,https://pypi.org/project/dpdata,2025-03-20 03:32:07.000,40.0,27224.0,27230.0,https://anaconda.org/deepmodeling/dpdata,2025-03-25 16:24:58.046,302.0,1.0,,,,,,,,,,,,,,,,,,,,, +68,SevenNet,,uip,https://github.com/MDIL-SNU/SevenNet,SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular..,23,True,GPL-3.0,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2025-06-21 05:19:20.000000,2025-06-21 05:19:16,1041.0,69.0,38.0,6.0,143.0,16.0,48.0,187.0,2025-05-19 04:31:57.000,0.11.2,16.0,16.0,sevenn,,14.0,,https://pypi.org/project/sevenn,2025-05-20 05:28:51.000,14.0,9430.0,9556.0,,,,2.0,,,,,,,3038.0,,,,,,,,,,,,,, +69,Crystal Toolkit,,visualization,https://github.com/materialsproject/crystaltoolkit,Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials..,23,True,MIT,,materialsproject/crystaltoolkit,https://github.com/materialsproject/crystaltoolkit,2017-07-25 21:06:36,2025-06-30 08:05:32.000000,2025-06-11 22:57:07,3300.0,7.0,60.0,8.0,332.0,67.0,63.0,174.0,2024-10-22 23:25:31.000,2024.10.22,64.0,31.0,crystal-toolkit,,55.0,43.0,https://pypi.org/project/crystal-toolkit,2025-06-11 23:45:31.000,12.0,1457.0,1457.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +70,SMACT,,materials-discovery,https://github.com/WMD-group/SMACT,Python package to aid materials design and informatics.,23,True,MIT,"['htc', 'structure-prediction', 'electrostatics']",WMD-group/SMACT,https://github.com/WMD-group/SMACT,2013-11-04 17:12:56,2025-07-02 13:02:46.000000,2025-04-07 18:59:51,1858.0,2.0,27.0,22.0,372.0,6.0,57.0,111.0,2025-04-02 23:35:49.000,3.1.0,29.0,46.0,smact,conda-forge/smact,63.0,58.0,https://pypi.org/project/smact,2025-04-02 23:35:49.000,5.0,4118.0,4380.0,https://anaconda.org/conda-forge/smact,2025-04-22 14:58:47.500,5768.0,1.0,-3.0,,,,,,,,,,,,,,,,,,,, +71,Metatensor,,data-structures,https://github.com/metatensor/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,23,True,BSD-3-Clause,"['ml-iap', 'md', 'lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",metatensor/metatensor,https://github.com/metatensor/metatensor,2022-03-01 15:58:28,2025-07-03 08:45:25.000000,2025-07-03 08:45:24,988.0,57.0,22.0,15.0,682.0,68.0,183.0,77.0,2025-04-25 12:46:05.000,metatensor-learn-v0.3.2,66.0,30.0,metatensor,,14.0,14.0,https://pypi.org/project/metatensor,2024-01-26 17:26:59.000,,1113.0,3313.0,,,,1.0,,,,,,,46203.0,,,,,,,,,,,,,, +72,Best-of Machine Learning with Python,,community,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,22,True,CC-BY-4.0,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2025-07-03 15:41:29.000000,2025-07-03 15:41:27,589.0,26.0,2793.0,440.0,321.0,27.0,34.0,21432.0,2025-07-03 15:41:36.000,2025.07.03,100.0,54.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +73,MEGNet,,ml-iap,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,22,False,BSD-3-Clause,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000000,2023-04-27 02:39:17,1146.0,,152.0,23.0,314.0,21.0,57.0,535.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,98.0,94.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,578.0,578.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +74,TorchMD-NET,,ml-iap,https://github.com/torchmd/torchmd-net,Training neural network potentials.,22,True,MIT,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2025-06-12 06:27:11.000000,2025-05-05 14:30:55,1415.0,15.0,84.0,6.0,243.0,45.0,88.0,416.0,2025-05-05 14:23:39.000,2.4.10,35.0,17.0,,conda-forge/torchmd-net,,,,,,,24614.0,https://anaconda.org/conda-forge/torchmd-net,2025-05-30 08:27:44.700,516805.0,1.0,,,,,,,136.0,,,,,,,,,,,,,, +75,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis,"This repository is no longer maintained. For the latest updates and continued development, please visit:..",22,True,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2025-06-27 04:21:46.000000,2025-06-27 04:21:46,2111.0,1.0,130.0,25.0,239.0,49.0,45.0,348.0,2025-06-24 13:33:58.000,2025.5.30,113.0,15.0,jarvis-tools,conda-forge/jarvis-tools,35.0,,https://pypi.org/project/jarvis-tools,2025-06-24 13:33:58.000,35.0,14712.0,16588.0,https://anaconda.org/conda-forge/jarvis-tools,2025-04-22 14:57:38.490,106950.0,2.0,,,,,,,,,,,,,,,,,,,,, +76,AtomAI,,general-tool,https://github.com/pycroscopy/atomai,Deep and Machine Learning for Microscopy.,22,True,MIT,"['computer-vision', 'unsupervised', 'experimental-data']",pycroscopy/atomai,https://github.com/pycroscopy/atomai,2020-09-04 06:08:25,2025-06-24 07:57:00.000000,2025-06-23 22:29:30,1461.0,49.0,41.0,8.0,78.0,11.0,9.0,213.0,2025-06-24 07:57:00.000,0.8.1,31.0,6.0,atomai,,11.0,10.0,https://pypi.org/project/atomai,2025-06-23 22:33:28.000,1.0,708.0,708.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +77,GPUMD,,md,https://github.com/brucefan1983/GPUMD,GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials..,21,True,GPL-3.0,"['ml-iap', 'lang-cpp', 'electrostatics']",brucefan1983/GPUMD,https://github.com/brucefan1983/GPUMD,2017-07-14 15:32:56,2025-07-03 09:54:06.000000,2025-07-02 08:00:37,5557.0,508.0,140.0,27.0,822.0,17.0,212.0,602.0,2025-05-23 20:11:21.000,4.2,45.0,50.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +78,exmol,,xai,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,21,True,MIT,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2025-05-08 18:45:03.000000,2025-05-08 18:03:28,204.0,3.0,44.0,7.0,86.0,6.0,66.0,333.0,2025-05-08 18:05:32.000,3.3.0,32.0,9.0,exmol,,29.0,26.0,https://pypi.org/project/exmol,2025-05-08 18:05:32.000,3.0,4893.0,4893.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +79,Molfeat,,general-tool,https://github.com/datamol-io/molfeat,molfeat - the hub for all your molecular featurizers.,21,True,Apache-2.0,"['cheminformatics', 'rep-eng', 'rep-learn', 'generative', 'language-models', 'pretrained']",datamol-io/molfeat,https://github.com/datamol-io/molfeat,2023-03-13 19:39:29,2025-05-30 17:41:45.733000,2025-05-27 01:51:33,297.0,1.0,23.0,9.0,56.0,15.0,44.0,214.0,2025-05-27 02:53:26.000,0.11.0,21.0,19.0,molfeat,conda-forge/molfeat,82.0,69.0,https://pypi.org/project/molfeat,2025-05-27 02:53:23.000,13.0,3450.0,4565.0,https://anaconda.org/conda-forge/molfeat,2025-05-30 17:41:45.733,31232.0,2.0,,,,,,,,,,,,,,,,,,,,, +80,MatCalc,,ml-iap,https://github.com/materialsvirtuallab/matcalc,A python library for calculating materials properties from the PES.,21,True,BSD-3-Clause,"['workflows', 'benchmarking', 'uip', 'pretrained', 'model-repository']",materialsvirtuallab/matcalc,https://github.com/materialsvirtuallab/matcalc,2023-07-25 02:04:04,2025-06-30 19:28:55.000000,2025-06-24 14:45:57,821.0,153.0,23.0,7.0,65.0,1.0,10.0,95.0,2025-05-10 16:23:11.000,0.4.1,16.0,17.0,matcalc,,16.0,10.0,https://pypi.org/project/matcalc,2025-05-10 16:38:00.000,6.0,4575.0,4575.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +81,Scikit-Matter,,general-tool,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,21,True,BSD-3-Clause,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2025-07-01 09:37:50.508000,2025-06-30 12:30:30,404.0,15.0,22.0,15.0,187.0,18.0,60.0,84.0,2025-06-30 12:33:21.000,0.3.1,9.0,18.0,skmatter,conda-forge/skmatter,18.0,14.0,https://pypi.org/project/skmatter,2025-06-30 12:33:18.000,4.0,2236.0,2362.0,https://anaconda.org/conda-forge/skmatter,2025-07-01 09:37:50.508,3537.0,2.0,,,,,,,,,,,,,,,,,,,,, +82,KLIFF,,ml-iap,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,21,True,LGPL-2.1,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2025-07-01 20:54:56.000000,2025-06-02 15:49:01,1263.0,52.0,21.0,5.0,172.0,24.0,33.0,37.0,2025-04-11 15:12:06.000,1.0.1,21.0,14.0,kliff,conda-forge/kliff,4.0,4.0,https://pypi.org/project/kliff,2025-04-11 14:48:12.000,,89.0,3013.0,https://anaconda.org/conda-forge/kliff,2025-04-22 14:57:17.390,166699.0,1.0,,,,,,,,,,,,,,,,,,,,, +83,janus-core,,ml-iap,https://github.com/stfc/janus-core,Tools for machine learnt interatomic potentials.,21,True,BSD-3-Clause,"['benchmarking', 'workflows', 'structure-optimization', 'md', 'transport-phenomena']",stfc/janus-core,https://github.com/stfc/janus-core,2024-02-07 11:12:56,2025-06-26 11:07:41.000000,2025-06-26 11:07:41,365.0,47.0,12.0,7.0,318.0,39.0,212.0,32.0,2025-06-13 17:13:48.000,0.8.1,36.0,9.0,janus-core,,13.0,10.0,https://pypi.org/project/janus-core,2025-06-13 17:13:48.000,3.0,612.0,620.0,,,,1.0,,,,,,,152.0,,,,,,,,,,,,,, +84,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,20,False,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2024-08-12 14:01:29.000000,2024-04-04 13:37:56,1437.0,,3219.0,296.0,547.0,341.0,574.0,14366.0,,,,114.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +85,DM21,,ml-dft,https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,False,Apache-2.0,,google-deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2025-06-18 19:25:59.000000,2023-06-02 17:04:50,369.0,,2677.0,328.0,258.0,266.0,141.0,14072.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +86,DIG: Dive into Graphs,,rep-learn,https://github.com/divelab/DIG,A library for graph deep learning research.,20,False,GPL-3.0,,divelab/DIG,https://github.com/divelab/DIG,2020-10-30 03:51:15,2024-07-15 07:18:56.000000,2024-02-04 20:37:53,1083.0,,286.0,30.0,42.0,37.0,177.0,1966.0,2023-04-07 20:33:15.000,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,2022-06-27 05:08:24.000,,471.0,471.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +87,OpenML,,community,https://github.com/openml/OpenML,Open Machine Learning.,20,True,BSD-3,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2025-06-28 22:02:22.000000,2025-06-28 22:02:21,2332.0,3.0,95.0,47.0,208.0,370.0,563.0,697.0,2025-06-20 12:29:29.000,1.1.0,2.0,35.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +88,Orb Models,,uip,https://github.com/orbital-materials/orb-models,ORB forcefield models from Orbital Materials.,20,True,https://github.com/orbital-materials/orb-models/blob/main/LICENSE,"['ml-iap', 'pretrained']",orbital-materials/orb-models,https://github.com/orbital-materials/orb-models,2024-08-30 15:27:25,2025-04-30 09:08:38.000000,2025-04-30 09:01:36,52.0,13.0,61.0,9.0,58.0,3.0,44.0,451.0,2025-04-30 09:08:38.000,0.5.4,13.0,11.0,orb-models,,31.0,19.0,https://pypi.org/project/orb-models,2025-04-30 09:08:38.000,12.0,11153.0,11153.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +89,FLARE,,active-learning,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,20,True,MIT,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2025-05-30 14:43:35.000000,2025-05-24 21:04:03,4595.0,15.0,72.0,19.0,206.0,36.0,183.0,328.0,2024-03-25 15:48:12.000,1.3.0,6.0,44.0,,,12.0,12.0,,,,,0.0,,,,2.0,,,,,,,9.0,,,,,,,,,,,,,, +90,CHGNet,,uip,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,20,True,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2025-04-14 21:44:10.000000,2025-04-14 21:44:04,437.0,3.0,81.0,7.0,100.0,3.0,69.0,313.0,2024-09-16 22:18:58.000,0.4.0,17.0,11.0,chgnet,,80.0,59.0,https://pypi.org/project/chgnet,2024-09-16 22:18:58.000,21.0,17228.0,17228.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +91,KFAC-JAX,,math,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,20,True,Apache-2.0,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2025-07-02 11:53:51.000000,2025-07-02 11:53:47,303.0,22.0,27.0,11.0,329.0,19.0,11.0,280.0,2025-05-20 17:49:21.000,0.0.7,6.0,19.0,kfac-jax,,13.0,11.0,https://pypi.org/project/kfac-jax,2025-05-20 17:49:21.000,2.0,1019.0,1019.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +92,cuEquivariance,,math,https://github.com/NVIDIA/cuEquivariance,cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used..,20,True,Apache-2.0,['rep-learn'],NVIDIA/cuEquivariance,https://github.com/NVIDIA/cuEquivariance,2024-10-22 15:36:00,2025-07-03 14:26:11.000000,2025-06-19 14:32:42,74.0,10.0,17.0,17.0,99.0,5.0,21.0,250.0,2025-06-19 14:56:26.000,0.5.1,7.0,5.0,cuequivariance,conda-forge/cuequivariance,6.0,,https://pypi.org/project/cuequivariance,2025-06-19 14:56:43.000,6.0,26383.0,27510.0,https://anaconda.org/conda-forge/cuequivariance,2025-06-10 16:52:16.929,6765.0,1.0,,,,,,,,,,,,,,,,,,,,, +93,MatBench Discovery,,community,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,20,True,MIT,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2025-07-02 12:09:19.000000,2025-07-02 12:09:15,513.0,39.0,40.0,12.0,183.0,4.0,60.0,165.0,2024-09-11 19:00:12.000,1.3.1,10.0,21.0,matbench-discovery,,4.0,4.0,https://pypi.org/project/matbench-discovery,2024-09-11 19:00:12.000,,1047.0,1047.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +94,DADApy,,unsupervised,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,20,True,Apache-2.0,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2025-07-02 08:50:59.000000,2025-06-06 12:12:55,962.0,30.0,21.0,8.0,123.0,11.0,28.0,130.0,2025-04-11 13:27:58.000,0.3.3,8.0,21.0,dadapy,,13.0,13.0,https://pypi.org/project/dadapy,2025-04-11 13:21:59.000,,119.0,119.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +95,mlcolvar,,md,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,20,True,MIT,['sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2025-06-20 09:20:56.000000,2025-06-17 09:54:32,1193.0,32.0,28.0,4.0,110.0,18.0,65.0,114.0,2025-02-19 13:56:14.000,1.2.2,13.0,10.0,mlcolvar,,7.0,7.0,https://pypi.org/project/mlcolvar,2025-02-19 13:56:14.000,,398.0,398.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +96,MALA,,ml-dft,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,20,True,BSD-3-Clause,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2025-06-05 09:41:07.000000,2025-06-05 09:39:57,2842.0,92.0,28.0,8.0,371.0,30.0,277.0,93.0,2025-06-02 11:24:27.000,1.4.0,11.0,47.0,,,2.0,2.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +97,Metatrain,,ml-iap,https://github.com/metatensor/metatrain,Training and evaluating machine learning models for atomistic systems.,20,True,BSD-3-Clause,"['workflows', 'benchmarking', 'rep-eng', 'rep-learn']",metatensor/metatrain,https://github.com/metatensor/metatrain,2023-11-20 14:51:11,2025-07-03 16:00:34.000000,2025-07-03 11:10:59,420.0,71.0,11.0,14.0,451.0,70.0,132.0,33.0,2025-06-11 14:25:21.000,2025.8.1,11.0,17.0,metatrain,,8.0,6.0,https://pypi.org/project/metatrain,2025-06-11 14:25:17.000,2.0,2000.0,2002.0,,,,2.0,,,,,,,12.0,,,,,,,,,,,,,, +98,Allegro,,ml-iap,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,19,True,MIT,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2025-07-01 03:18:13.000000,2025-07-01 03:14:05,309.0,69.0,57.0,21.0,13.0,4.0,41.0,407.0,2025-07-01 03:18:13.000,0.7.0,9.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +99,DeepQMC,,ml-wft,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,19,True,MIT,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2025-05-28 15:52:09.000000,2025-05-28 15:52:08,1480.0,7.0,64.0,20.0,164.0,3.0,53.0,380.0,2024-09-24 11:12:20.000,1.2.0,12.0,13.0,deepqmc,,3.0,3.0,https://pypi.org/project/deepqmc,2024-09-24 11:12:20.000,,203.0,203.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +100,ATOM3D,,datasets,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,19,False,MIT,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000000,2023-03-02 18:20:29,798.0,,35.0,16.0,6.0,22.0,40.0,310.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,57.0,57.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,633.0,633.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +101,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn,"This repository is no longer maintained. For the latest updates and continued development, please visit:..",19,True,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2025-06-27 04:23:38.000000,2025-06-27 04:23:38,790.0,2.0,96.0,8.0,116.0,48.0,28.0,273.0,2025-04-02 06:26:22.000,2025.4.1,52.0,7.0,alignn,,34.0,23.0,https://pypi.org/project/alignn,2025-04-02 06:13:55.000,11.0,6358.0,6358.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +102,TorchSim,,md,https://github.com/Radical-AI/torch-sim,"Torch-native, batchable, atomistic simulation.",19,True,MIT,"['htc', 'uip', 'ml-iap', 'structure-optimization']",Radical-AI/torch-sim,https://github.com/Radical-AI/torch-sim,2025-03-03 15:13:57,2025-06-11 23:16:04.000000,2025-06-10 17:58:04,136.0,56.0,30.0,5.0,138.0,20.0,45.0,244.0,2025-06-10 18:23:51.000,0.2.2,4.0,15.0,torch-sim-atomistic,,,,https://pypi.org/project/torch-sim-atomistic,2025-06-10 18:23:51.000,,4958.0,4958.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +103,e3nn-jax,,rep-learn,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,19,True,Apache-2.0,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2025-01-23 21:45:38.000000,2025-01-23 21:45:38,1036.0,,19.0,12.0,62.0,3.0,22.0,209.0,2024-08-14 05:14:56.000,0.20.7,43.0,8.0,e3nn-jax,,84.0,71.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,7719.0,7719.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +104,sGDML,,ml-iap,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,19,True,MIT,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2025-06-13 12:41:44.000000,2025-06-13 12:26:53,208.0,3.0,37.0,8.0,12.0,11.0,11.0,152.0,2025-06-13 12:27:35.000,1.0.3,22.0,8.0,sgdml,,15.0,13.0,https://pypi.org/project/sgdml,2025-06-13 12:41:44.000,2.0,448.0,448.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +105,Chemiscope,,visualization,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,19,True,BSD-3-Clause,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2025-06-16 14:10:10.000000,2025-06-07 19:48:03,798.0,13.0,40.0,19.0,277.0,41.0,105.0,147.0,2025-05-27 14:51:46.000,0.8.6,25.0,25.0,,,9.0,6.0,,,,,119.0,,,,2.0,,,,,,,508.0,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,112.0,,,, +106,ChemBench,,language-models,https://github.com/lamalab-org/chembench,How good are LLMs at chemistry?.,19,True,MIT,"['benchmarking', 'multimodal']",lamalab-org/chembench,https://github.com/lamalab-org/chembench,2023-05-16 08:18:26,2025-07-01 10:28:50.000000,2025-06-03 14:51:54,1127.0,2.0,12.0,4.0,508.0,52.0,281.0,97.0,2025-02-27 08:39:21.000,0.3.0,2.0,13.0,chembench,,3.0,3.0,https://pypi.org/project/chembench,2025-02-27 08:39:21.000,,3955.0,3955.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +107,apax,,ml-iap,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,19,True,MIT,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2025-07-02 12:16:30.000000,2025-07-01 09:12:56,2256.0,152.0,3.0,3.0,315.0,15.0,134.0,20.0,2025-06-17 13:17:46.000,0.12.1,15.0,9.0,apax,,4.0,4.0,https://pypi.org/project/apax,2025-06-17 13:17:46.000,,410.0,410.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +108,Graph-based Deep Learning Literature,,community,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,MIT,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2025-05-23 15:05:41.000000,2025-05-23 15:05:35,7781.0,36.0,774.0,249.0,21.0,,15.0,4932.0,,,,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +109,MACE-FOUNDATION models,,uip,https://github.com/ACEsuit/mace-foundations,"MACE foundation models (MP, OMAT, Matpes).",18,True,MIT,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-foundations,https://github.com/ACEsuit/mace-foundations,2024-01-11 10:55:55,2025-07-01 16:11:24.000000,2025-06-12 12:52:19,22.0,2.0,270.0,15.0,1.0,4.0,14.0,699.0,2025-07-01 16:11:24.000,mace_omol_0,11.0,2.0,mace-torch,,36.0,,https://pypi.org/project/mace-torch,2025-05-01 15:43:47.000,36.0,27464.0,35725.0,,,,3.0,,,,,,,148703.0,,,,,,,,,,,,,, +110,MatterSim,,uip,https://github.com/microsoft/mattersim,"MatterSim: A deep learning atomistic model across elements, temperatures and pressures.",18,True,MIT,"['ml-iap', 'active-learning', 'multimodal', 'phase-transition', 'pretrained']",microsoft/mattersim,https://github.com/microsoft/mattersim,2024-09-06 07:34:07,2025-05-19 03:11:00.000000,2025-05-19 03:11:00,128.0,6.0,57.0,10.0,84.0,12.0,18.0,422.0,2025-02-21 06:17:25.000,1.1.2,18.0,17.0,mattersim,,2.0,,https://pypi.org/project/mattersim,2025-02-21 06:17:25.000,2.0,14036.0,14039.0,,,,3.0,,,,,,,27.0,,,,,,,,,,,,,, +111,openmm-torch,,md,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,18,True,https://github.com/openmm/openmm-torch#license,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2025-06-20 19:12:52.877000,2025-02-20 17:01:17,81.0,,29.0,10.0,71.0,29.0,68.0,200.0,2025-02-24 20:47:52.000,1.5.1,18.0,9.0,,conda-forge/openmm-torch,,,,,,,16495.0,https://anaconda.org/conda-forge/openmm-torch,2025-06-20 19:12:52.877,890781.0,2.0,,,,,,,,,,,,,,,,,,,,, +112,FitSNAP,,md,https://github.com/FitSNAP/FitSNAP,Software for generating machine-learning interatomic potentials for LAMMPS.,18,True,GPL-2.0,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2025-07-03 15:34:58.000000,2025-07-03 15:34:58,1439.0,8.0,58.0,6.0,190.0,16.0,61.0,168.0,2023-06-28 16:00:48.000,3.1.0,7.0,25.0,,conda-forge/fitsnap3,,,,,,,236.0,https://anaconda.org/conda-forge/fitsnap3,2025-04-22 14:57:40.914,13223.0,2.0,,,,,,,15.0,,,,,,,,,,,,,, +113,ChemML,,rep-eng,https://github.com/hachmannlab/chemml,ChemML is a machine learning and informatics program suite for the chemical and materials sciences.,18,True,BSD-3-Clause,"['cheminformatics', 'active-learning', 'workflows']",hachmannlab/chemml,https://github.com/hachmannlab/chemml,2017-12-07 04:48:18,2025-05-05 18:09:07.000000,2025-05-05 18:09:07,904.0,20.0,32.0,12.0,25.0,7.0,6.0,168.0,2021-11-11 20:57:19.000,1.0,17.0,15.0,chemml,,10.0,8.0,https://pypi.org/project/chemml,2023-10-08 16:07:41.000,2.0,253.0,253.0,,,,1.0,,,,,,,14.0,,,,,,,,,,,,,, +114,MatBench,,community,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,18,False,MIT,"['datasets', 'benchmarking', 'model-repository']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2024-08-20 17:26:52.000000,2024-01-20 09:41:36,772.0,,48.0,6.0,300.0,39.0,26.0,158.0,2022-07-27 04:40:26.000,0.6,5.0,26.0,matbench,,26.0,24.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,289.0,289.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +115,Elementari,,visualization,https://github.com/janosh/matterviz,"Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,..",18,True,MIT,['lang-js'],janosh/elementari,https://github.com/janosh/matterviz,2022-06-01 15:29:36,2025-07-03 17:42:19.000000,2025-06-30 11:51:46,244.0,53.0,16.0,5.0,74.0,2.0,7.0,158.0,2025-06-19 21:03:10.748,0.4.2,34.0,2.0,,,6.0,4.0,,,,,693.0,,,,3.0,,,,,,,,janosh/matterviz,,,,,elementari,https://www.npmjs.com/package/elementari,2025-06-19 21:03:10.748,2.0,693.0,,,, +116,kgcnn,,rep-learn,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",18,True,MIT,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2025-01-08 13:58:21.000000,2025-01-05 13:20:53,3100.0,,31.0,5.0,30.0,13.0,74.0,116.0,2025-01-08 13:58:21.000,4.0.2,29.0,7.0,kgcnn,,23.0,20.0,https://pypi.org/project/kgcnn,2025-01-08 13:58:21.000,3.0,371.0,371.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +117,Graph-PES,,ml-iap,https://github.com/jla-gardner/graph-pes,train and use graph-based ML models of potential energy surfaces.,18,True,MIT,"['rep-learn', 'uip', 'md', 'pretrained']",jla-gardner/graph-pes,https://github.com/jla-gardner/graph-pes,2023-10-09 07:18:23,2025-06-30 11:00:47.000000,2025-06-25 15:03:51,346.0,58.0,7.0,1.0,135.0,4.0,13.0,97.0,2025-06-25 15:06:35.000,0.1.8,45.0,4.0,graph-pes,,5.0,3.0,https://pypi.org/project/graph-pes,2025-06-25 15:06:35.000,2.0,1876.0,1876.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +118,SpheriCart,,math,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,18,True,MIT,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2025-05-20 09:45:01.000000,2025-05-20 09:42:01,420.0,6.0,15.0,5.0,144.0,23.0,25.0,86.0,2025-04-28 08:46:00.000,1.0.2,16.0,12.0,sphericart,,7.0,7.0,https://pypi.org/project/sphericart,2025-04-28 08:46:00.000,,2439.0,2453.0,,,,2.0,,,,,,,394.0,,,,,,,,,,,,,, +119,ZnDraw,,visualization,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",18,True,EPL-2.0,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2025-06-30 19:20:21.000000,2025-02-18 15:21:51,470.0,,4.0,1.0,427.0,99.0,265.0,43.0,2025-02-19 08:30:51.000,0.5.10,78.0,7.0,zndraw,,16.0,11.0,https://pypi.org/project/zndraw,2025-02-19 08:30:51.000,5.0,773.0,773.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +120,Garden,,community,https://thegardens.ai/,FAIR AI/ML Model Publishing Framework.,18,True,MIT,['model-repository'],Garden-AI/garden,https://github.com/Garden-AI/garden,2022-10-05 17:05:42,2025-06-16 19:05:26.000000,2025-06-16 17:27:06,395.0,5.0,4.0,7.0,246.0,8.0,337.0,33.0,2025-06-16 19:05:26.000,3.1.2,70.0,13.0,garden-ai,,6.0,6.0,https://pypi.org/project/garden-ai,2025-06-16 19:05:26.000,,575.0,575.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +121,IPSuite,,active-learning,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,18,True,EPL-2.0,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2025-06-30 19:20:21.000000,2025-06-20 08:55:57,537.0,19.0,11.0,2.0,268.0,82.0,83.0,23.0,2025-06-17 11:41:58.000,0.2.7,15.0,8.0,ipsuite,,12.0,8.0,https://pypi.org/project/ipsuite,2025-06-17 11:41:58.000,4.0,321.0,321.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +122,paper-qa,,language-models,https://futurehouse.gitbook.io/futurehouse-cookbook,LLM Chain for answering questions from docs.,17,True,,['ai-agent'],whitead/paper-qa,,,2025-05-28 03:57:18.000000,,,,727.0,,,,,7400.0,2025-05-28 03:57:18.000,5.21.0,150.0,,paper-qa,,13.0,,https://pypi.org/project/paper-qa,2025-05-28 03:57:18.000,13.0,13947.0,13947.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +123,MatterGen,,materials-discovery,https://github.com/microsoft/mattergen,Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table..,17,True,MIT,"['generative', 'structure-prediction', 'pretrained']",microsoft/mattergen,https://github.com/microsoft/mattergen,2024-11-27 11:09:38,2025-06-13 10:17:28.000000,2025-06-13 10:17:28,81.0,11.0,233.0,26.0,41.0,,110.0,1403.0,2025-01-17 17:14:04.000,1.0.0,1.0,9.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +124,Uni-Mol,,rep-learn,https://github.com/deepmodeling/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,17,True,MIT,['pretrained'],deepmodeling/Uni-Mol,https://github.com/deepmodeling/Uni-Mol,2022-05-22 13:26:41,2025-05-29 07:41:36.000000,2025-05-29 07:41:36,165.0,6.0,142.0,19.0,140.0,99.0,110.0,895.0,2024-07-06 07:05:10.000,0.2.1,3.0,20.0,,,,,,,,,560.0,,,,2.0,,,,,,,18499.0,,,,,,,,,,,,,, +125,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,17,True,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2024-10-31 17:00:20.000000,2024-10-31 17:00:20,247.0,,54.0,19.0,35.0,38.0,39.0,445.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,3499.0,3499.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +126,gpax,,math,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,17,False,MIT,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-10-21 06:29:28.000000,2024-05-21 08:13:54,787.0,,27.0,7.0,69.0,9.0,32.0,225.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,5.0,5.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,232.0,232.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +127,QML,,general-tool,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,17,True,MIT,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2024-12-08 14:49:19.000000,2024-12-08 14:49:19,161.0,,84.0,22.0,101.0,38.0,21.0,205.0,2018-03-02 11:36:41.000,0.4.0,34.0,10.0,qml,,,,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,265.0,265.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +128,OpenBioML ChemNLP,,language-models,https://github.com/OpenBioML/chemnlp,ChemNLP project.,17,True,MIT,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2025-06-30 17:06:36.000000,2024-08-19 19:00:21,372.0,,45.0,4.0,286.0,112.0,140.0,161.0,2023-08-07 12:49:57.000,2023.7.1,6.0,27.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,71.0,71.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +129,Open Databases Integration for Materials Design (OPTIMADE),,datasets,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,17,True,CC-BY-4.0,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2025-06-10 05:29:40.000000,2025-06-10 05:29:24,1832.0,6.0,37.0,21.0,304.0,79.0,172.0,90.0,2024-06-10 16:32:29.000,1.2.0,9.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +130,Autoplex,,ml-iap,https://github.com/autoatml/autoplex,Code for automated fitting of machine learned interatomic potentials.,17,True,GPL-3.0,"['benchmarking', 'workflows']",autoatml/autoplex,https://github.com/autoatml/autoplex,2023-07-26 15:36:09,2025-07-03 00:56:50.000000,2025-07-01 12:18:25,1958.0,60.0,14.0,3.0,284.0,35.0,91.0,85.0,2025-07-01 10:41:49.000,0.1.4,13.0,12.0,autoplex,,2.0,2.0,https://pypi.org/project/autoplex,2025-07-01 10:47:30.000,,104.0,104.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +131,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,17,True,https://github.com/materialsproject/pyrho,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2025-06-28 00:19:26.000000,2024-10-22 22:19:21,292.0,,9.0,8.0,124.0,2.0,3.0,40.0,2024-10-22 22:21:31.000,0.4.5,29.0,10.0,mp-pyrho,,37.0,32.0,https://pypi.org/project/mp-pyrho,2024-10-22 22:21:31.000,5.0,14159.0,14159.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +132,dlpack,,data-structures,https://github.com/dmlc/dlpack,common in-memory tensor structure.,16,True,Apache-2.0,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2025-06-11 00:00:22.000000,2025-06-10 23:59:02,90.0,3.0,144.0,51.0,91.0,29.0,48.0,1025.0,2025-03-11 15:05:00.000,1.1,11.0,33.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +133,ChemCrow,,language-models,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,16,True,MIT,['ai-agent'],ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2024-12-19 17:47:04.000000,2024-12-19 17:47:03,121.0,,117.0,18.0,31.0,9.0,15.0,777.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,10.0,10.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,442.0,442.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +134,FermiNet,,ml-wft,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,16,True,Apache-2.0,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2025-06-02 16:31:03.000000,2025-06-02 16:29:57,257.0,4.0,149.0,32.0,32.0,3.0,65.0,771.0,,,,22.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +135,GT4SD,,generative,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",16,True,MIT,"['pretrained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2025-02-19 13:51:09.000000,2025-02-19 13:33:29,301.0,,75.0,15.0,152.0,14.0,103.0,355.0,2024-09-12 13:44:36.000,1.4.3,87.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2025-02-19 13:33:58.000,,800.0,800.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +136,ChemDataExtractor,,language-models,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,16,False,MIT,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2025-03-25 16:27:55.911000,2017-02-21 23:20:23,106.0,,114.0,18.0,16.0,21.0,10.0,329.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,141.0,133.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,766.0,829.0,https://anaconda.org/chemdataextractor/chemdataextractor,2025-03-25 16:27:55.911,3442.0,2.0,,,,,,,3292.0,,,,,,,,,,,,,, +137,Neural Force Field,,ml-iap,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,16,True,MIT,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2025-06-04 04:50:03.000000,2025-05-01 01:47:12,3221.0,14.0,56.0,6.0,15.0,4.0,18.0,277.0,2024-05-29 21:15:00.000,1.0.0,1.0,45.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +138,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,16,False,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000000,2022-01-06 19:39:49,1666.0,,50.0,11.0,233.0,41.0,139.0,157.0,2020-07-28 02:19:07.000,1.0.3.20200727,17.0,13.0,automatminer,,10.0,10.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,144.0,144.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +139,MODNet,,rep-eng,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,16,True,MIT,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2025-05-02 11:38:28.000000,2025-05-02 10:11:44,291.0,1.0,34.0,6.0,200.0,32.0,31.0,93.0,2025-03-19 14:30:12.000,0.4.5,22.0,11.0,,,11.0,11.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +140,DeePTB,,ml-esm,https://github.com/deepmodeling/DeePTB,DeePTB: A deep learning package for tight-binding Hamiltonian with ab initio accuracy.,16,True,LGPL-3.0,['ml-dft'],deepmodeling/DeePTB,https://github.com/deepmodeling/DeePTB,2023-07-11 03:19:42,2025-06-30 16:44:53.000000,2025-06-30 16:44:06,790.0,19.0,20.0,3.0,209.0,19.0,33.0,79.0,2025-05-07 14:03:15.000,2.2.0,12.0,11.0,dptb,,6.0,4.0,https://pypi.org/project/dptb,2025-05-07 14:03:15.000,2.0,134.0,134.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +141,load-atoms,,datasets,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,16,True,MIT,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-12-16 09:39:30.000000,2024-12-16 09:39:30,295.0,,4.0,1.0,45.0,2.0,30.0,45.0,2024-12-13 14:43:20.000,0.3.9,46.0,4.0,load-atoms,,10.0,8.0,https://pypi.org/project/load-atoms,2024-12-13 14:43:20.000,2.0,1950.0,1950.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +142,ElementEmbeddings,,rep-eng,https://github.com/WMD-group/ElementEmbeddings,Python package to interact with high-dimensional representations of the chemical elements.,16,True,MIT,"['xai', 'unsupervised', 'visualization']",WMD-group/ElementEmbeddings,https://github.com/WMD-group/ElementEmbeddings,2022-05-17 14:25:41,2025-06-30 18:23:42.000000,2025-01-09 16:34:10,634.0,,4.0,4.0,159.0,5.0,17.0,43.0,2024-09-18 13:09:00.000,0.6.1,10.0,6.0,ElementEmbeddings,conda-forge/elementembeddings,6.0,6.0,https://pypi.org/project/ElementEmbeddings,2024-09-18 13:09:44.000,,1247.0,1429.0,https://anaconda.org/conda-forge/elementembeddings,2025-04-22 14:59:09.861,2008.0,1.0,,,,,,,,,,,,,,,,,,,,, +143,Graphormer,,rep-learn,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,15,False,MIT,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35.000000,2024-05-28 06:22:34,77.0,,345.0,29.0,46.0,92.0,68.0,2307.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +144,M3GNet,,uip,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,15,True,BSD-3-Clause,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2025-04-07 22:52:21.000000,2025-04-07 22:52:21,264.0,1.0,68.0,11.0,37.0,15.0,20.0,288.0,2022-11-17 23:25:35.000,0.2.4,16.0,16.0,m3gnet,,5.0,,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,710.0,710.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +145,matsciml,,rep-learn,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,15,True,MIT,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2025-03-24 15:37:05.000000,2025-03-24 15:37:05,2858.0,,27.0,6.0,278.0,24.0,43.0,175.0,2023-08-31 23:59:40.000,1.0.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +146,XenonPy,,general-tool,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,15,False,BSD-3-Clause,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2024-07-15 21:14:48.000000,2024-04-21 06:58:38,693.0,,59.0,9.0,183.0,22.0,66.0,141.0,2023-05-21 15:54:32.000,0.6.8,54.0,9.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,624.0,641.0,,,,3.0,,,,,,,1547.0,,,,,,,,,,,,,, +147,CatLearn,,rep-eng,https://github.com/SUNCAT-Center/CatLearn,,15,False,GPL-3.0,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2024-06-28 07:53:45.000000,2023-02-07 09:31:25,1960.0,,57.0,18.0,80.0,10.0,17.0,112.0,2020-03-27 09:27:26.000,0.6.2,27.0,22.0,catlearn,,7.0,6.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,124.0,124.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +148,MLatom,,general-tool,https://github.com/dralgroup/mlatom,AI-enhanced computational chemistry.,15,True,MIT,"['uip', 'ml-iap', 'md', 'ml-dft', 'ml-esm', 'transfer-learning', 'active-learning', 'spectroscopy', 'structure-optimization']",dralgroup/mlatom,https://github.com/dralgroup/mlatom,2023-08-16 13:47:48,2025-07-02 07:54:00.000000,2025-07-02 07:54:00,100.0,11.0,14.0,4.0,36.0,2.0,5.0,95.0,2025-06-26 08:34:06.000,3.18.1,32.0,6.0,mlatom,,,,https://pypi.org/project/mlatom,2025-07-02 07:08:41.000,,897.0,897.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +149,MLIPX - Machine-Learned Interatomic Potential eXploration,,ml-iap,https://github.com/basf/mlipx,Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned..,15,True,MIT,"['benchmarking', 'visualization', 'workflows']",basf/mlipx,https://github.com/basf/mlipx,2024-10-18 08:48:26,2025-07-01 07:50:58.000000,2025-07-01 07:50:55,82.0,11.0,7.0,5.0,70.0,2.0,12.0,88.0,2025-06-09 19:11:53.000,0.1.5,6.0,5.0,mlipx,,2.0,2.0,https://pypi.org/project/mlipx,2025-06-09 19:12:05.000,,184.0,184.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +150,OpenEquivariance,,math,https://github.com/PASSIONLab/OpenEquivariance,"OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product.",15,True,BSD-3-Clause,['rep-learn'],PASSIONLab/OpenEquivariance,https://github.com/PASSIONLab/OpenEquivariance,2024-07-28 05:02:35,2025-07-03 01:28:39.000000,2025-07-03 01:28:20,187.0,44.0,6.0,6.0,118.0,5.0,20.0,74.0,2025-06-22 23:42:32.000,0.3.0,2.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +151,Featomic,,rep-eng,https://github.com/metatensor/featomic,Computing representations for atomistic machine learning.,15,True,BSD-3-Clause,"['lang-rust', 'lang-cpp']",metatensor/featomic,https://github.com/metatensor/featomic,2020-09-24 14:28:34,2025-06-07 07:00:25.000000,2025-06-07 06:55:15,665.0,15.0,15.0,5.0,320.0,42.0,41.0,72.0,2025-05-22 13:29:14.000,featomic-torch-v0.7.0,9.0,16.0,,,,,,,,,28.0,,,,2.0,,,,,,,199.0,,,,,,,,,,,,,, +152,Ultra-Fast Force Fields (UF3),,ml-iap,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,15,True,Apache-2.0,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2025-06-12 06:57:44.000000,2024-10-04 15:08:06,731.0,,26.0,7.0,86.0,19.0,32.0,68.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,2.0,2.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,26.0,26.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +153,PET-MAD,,uip,https://github.com/lab-cosmo/pet-mad,"PET-MAD, a universal interatomic potential for advanced materials modeling.",15,True,BSD-3-Clause,"['ml-iap', 'md', 'rep-learn', 'transformer']",lab-cosmo/pet-mad,https://github.com/lab-cosmo/pet-mad,2025-03-18 09:35:31,2025-06-30 14:29:38.000000,2025-06-30 14:29:37,95.0,55.0,4.0,13.0,21.0,1.0,1.0,64.0,2025-06-26 15:19:14.000,1.3.0,8.0,8.0,pet-mad,conda-forge/pet-mad,7.0,4.0,https://pypi.org/project/pet-mad,2025-06-26 15:19:14.000,3.0,605.0,605.0,https://anaconda.org/conda-forge/pet-mad,,,3.0,,,,,,,,,,,,,,,,,,,,, +154,MatPES,,datasets,https://matpes.ai/,A foundational potential energy dataset for materials.,15,True,BSD-3-Clause,"['uip', 'ml-iap']",materialsvirtuallab/matpes,https://github.com/materialsvirtuallab/matpes,2024-11-19 17:37:45,2025-06-02 20:09:15.000000,2025-06-02 20:09:15,180.0,12.0,4.0,1.0,6.0,,5.0,38.0,2025-03-10 17:24:46.000,0.0.3,4.0,3.0,matpes,,,,https://pypi.org/project/matpes,2025-03-10 17:24:55.000,,249.0,249.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +155,Polynomials4ML.jl,,math,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",15,True,MIT,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2025-06-25 08:07:09.000000,2025-06-23 00:35:38,488.0,73.0,6.0,4.0,53.0,7.0,50.0,13.0,2025-05-02 18:31:05.000,0.4.0,21.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +156,benchmarking-gnns,,rep-learn,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks (JMLR 2023).,14,False,MIT,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000000,2022-05-10 13:22:20,45.0,,452.0,58.0,20.0,7.0,63.0,2600.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +157,AI for Science Resources,,community,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",14,True,GPL-3.0 license,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2025-07-01 16:25:20.000000,2025-07-01 16:25:19,524.0,28.0,76.0,18.0,6.0,3.0,24.0,648.0,,,,33.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +158,QH9,,datasets,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,A Quantum Hamiltonian Prediction Benchmark.,14,True,CC-BY-NC-SA-4.0,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2025-07-01 16:25:20.000000,2025-07-01 16:25:19,524.0,28.0,76.0,18.0,6.0,3.0,24.0,648.0,,,,33.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +159,Artificial Intelligence for Science (AIRS),,general-tool,https://github.com/divelab/AIRS,Artificial Intelligence Research for Science (AIRS).,14,True,GPL-3.0 license,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2025-07-01 16:25:20.000000,2025-07-01 16:25:19,524.0,28.0,76.0,18.0,6.0,3.0,24.0,648.0,,,,33.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +160,QHNet,,ml-dft,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,14,True,GPL-3.0,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2025-07-01 16:25:20.000000,2025-07-01 16:25:19,524.0,28.0,76.0,18.0,6.0,3.0,24.0,648.0,,,,33.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +161,GT4SD - Generative Toolkit for Scientific Discovery,,community,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,14,True,MIT,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2025-02-19 13:51:09.000000,2025-02-19 13:33:29,301.0,,75.0,15.0,152.0,14.0,103.0,355.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +162,MoLeR,,generative,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,14,False,MIT,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2024-01-05 14:31:05.000000,2024-01-03 14:28:02,67.0,,41.0,11.0,37.0,6.0,35.0,303.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,103.0,103.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +163,Psiflow,,md,https://github.com/molmod/psiflow,scalable molecular simulation.,14,True,MIT,"['ml-iap', 'active-learning', 'sampling']",molmod/psiflow,https://github.com/molmod/psiflow,2022-11-18 09:57:59,2025-06-30 16:42:46.000000,2025-06-30 16:42:46,933.0,32.0,12.0,4.0,24.0,12.0,44.0,136.0,2024-12-23 10:02:20.000,4.0.0,10.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +164,OpenMM-ML,,md,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,14,True,MIT,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2025-04-22 14:58:24.330000,2025-03-12 21:26:15,49.0,,26.0,16.0,37.0,25.0,37.0,114.0,2025-03-12 21:28:35.000,1.3,6.0,5.0,,conda-forge/openmm-ml,,,,,,,1064.0,https://anaconda.org/conda-forge/openmm-ml,2025-04-22 14:58:24.330,37250.0,3.0,,,,,,,,,,,,,,,,,,,,, +165,PMTransformer,,generative,https://github.com/hspark1212/MOFTransformer,"Universal Transfer Learning in Porous Materials, including MOFs.",14,False,MIT,"['transfer-learning', 'pretrained', 'transformer']",hspark1212/MOFTransformer,https://github.com/hspark1212/MOFTransformer,2021-12-11 06:30:12,2024-06-20 07:01:44.000000,2024-06-20 06:57:57,410.0,,15.0,4.0,127.0,1.0,43.0,104.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,9.0,8.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,919.0,919.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +166,NNPOps,,ml-iap,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,14,True,MIT,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2025-04-22 14:58:12.768000,2025-02-28 16:35:54,97.0,,18.0,6.0,68.0,22.0,35.0,93.0,2023-07-26 11:21:58.000,0.6,7.0,10.0,,conda-forge/nnpops,,,,,,,12690.0,https://anaconda.org/conda-forge/nnpops,2025-04-22 14:58:12.768,507632.0,2.0,,,,,,,,,,,,,,,,,,,,, +167,PyXtalFF,,ml-iap,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,14,False,MIT,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06.000000,2024-01-07 14:27:45,561.0,,23.0,8.0,4.0,12.0,51.0,90.0,2023-06-09 17:17:24.000,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,2022-12-21 20:21:00.409,,29.0,29.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +168,hippynn,,rep-learn,https://github.com/lanl/hippynn,python library for atomistic machine learning.,14,True,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2025-06-27 19:22:18.000000,2025-06-27 19:22:18,201.0,22.0,27.0,9.0,132.0,9.0,21.0,83.0,2025-06-16 22:20:17.000,hippynn-0.1.3,7.0,16.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +169,HydraGNN,,rep-learn,https://github.com/ORNL/HydraGNN,Distributed PyTorch implementation of multi-headed graph convolutional neural networks.,14,True,BSD-3,,ORNL/HydraGNN,https://github.com/ORNL/HydraGNN,2021-05-28 03:32:03,2025-06-24 14:09:31.000000,2025-06-13 18:28:50,724.0,4.0,30.0,8.0,294.0,17.0,37.0,83.0,2023-11-10 15:25:43.000,3.0,2.0,16.0,,,3.0,3.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +170,MLIP Arena Leaderboard,,uip,https://huggingface.co/spaces/atomind/mlip-arena,"Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics..",14,True,Apache-2.0,"['ml-iap', 'benchmarking']",atomind-ai/mlip-arena,https://github.com/atomind-ai/mlip-arena,2024-03-24 20:36:55,2025-06-18 04:46:00.000000,2025-06-13 21:52:10,228.0,20.0,4.0,,51.0,11.0,6.0,58.0,2025-05-22 22:02:29.000,0.1.1,5.0,3.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +171,OpenQDC,,datasets,https://www.openqdc.io/,Repository of Quantum Datasets Publicly Available.,14,True,CC-BY-4.0,,valence-labs/openQDC,https://github.com/valence-labs/OpenQDC,2023-09-18 00:56:43,2025-06-19 12:08:44.000000,2025-06-19 12:07:13,436.0,1.0,3.0,4.0,71.0,9.0,41.0,49.0,2024-08-09 20:48:47.000,0.1.2,3.0,10.0,openqdc,conda-forge/openqdc,4.0,4.0,https://pypi.org/project/openqdc,2024-08-09 20:48:43.000,,163.0,296.0,https://anaconda.org/conda-forge/openqdc,2025-04-22 14:59:15.340,1200.0,2.0,,,,,,,,,,,,,,,,,,,,, +172,synspace,,generative,https://github.com/whitead/synspace,Synthesis generative model.,14,True,MIT,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2025-04-24 22:25:19.000000,2025-04-24 22:25:19,36.0,9.0,4.0,3.0,2.0,2.0,2.0,45.0,2025-04-24 22:24:52.000,1.0.0,4.0,2.0,synspace,,40.0,36.0,https://pypi.org/project/synspace,2025-04-24 22:24:52.000,4.0,5200.0,5200.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +173,DP-GEN2,,active-learning,https://github.com/deepmodeling/dpgen2,2nd generation of the Deep Potential GENerator.,14,True,LGPL-3.0,"['ml-iap', 'md', 'workflows']",deepmodeling/dpgen2,https://github.com/deepmodeling/dpgen2,2022-02-28 02:41:16,2025-06-13 06:00:05.000000,2025-06-13 06:00:05,335.0,5.0,33.0,6.0,253.0,12.0,23.0,40.0,2023-01-28 03:43:25.000,0.0.7,7.0,15.0,,,6.0,6.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +174,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,14,True,MIT,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2025-06-04 19:17:34.000000,2025-06-04 19:16:53,431.0,1.0,5.0,1.0,56.0,16.0,3.0,17.0,2025-06-04 19:17:34.000,2.0.9,37.0,6.0,crabnet,,17.0,15.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,216.0,216.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +175,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,13,False,MIT,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000000,2021-09-06 05:23:38,25.0,,309.0,23.0,9.0,19.0,21.0,753.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +176,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics,"Curated list of known efforts in materials informatics, i.e. in modern materials science.",13,True,https://github.com/tilde-lab/awesome-materials-informatics#license,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2025-06-19 12:53:07.000000,2025-06-19 12:53:07,150.0,10.0,93.0,17.0,57.0,,9.0,450.0,2023-03-02 19:56:59.000,2023.03.02,1.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +177,n2p2,,ml-iap,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,True,GPL-3.0,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2025-03-17 11:32:05.000000,2025-03-17 11:03:50,542.0,,81.0,12.0,54.0,68.0,86.0,236.0,2024-11-22 23:47:00.000,2.3.0,12.0,13.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +178,So3krates (MLFF),,ml-iap,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,13,True,MIT,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2025-06-02 15:46:31.000000,2024-08-23 09:41:03,150.0,,28.0,6.0,28.0,6.0,7.0,121.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +179,MAST-ML,,general-tool,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,13,True,MIT,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2025-04-15 15:05:13.000000,2025-04-15 15:05:12,3316.0,1.0,61.0,13.0,37.0,32.0,191.0,119.0,2024-09-27 21:44:25.000,3.2.1,8.0,19.0,,,,,,,,,2.0,,,,3.0,,,,,,,145.0,,,,,,,,,,,,,, +180,SLICES and MatterGPT,,generative,https://github.com/xiaohang007/SLICES,"SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT,..",13,True,LGPL-2.1,"['rep-eng', 'language-models', 'transformer', 'materials-discovery', 'structure-prediction']",xiaohang007/SLICES,https://github.com/xiaohang007/SLICES,2023-04-27 04:01:11,2025-03-26 20:45:54.000000,2025-03-26 20:45:50,239.0,,43.0,2.0,1.0,4.0,13.0,114.0,2025-03-01 15:51:06.000,3.1.0,23.0,1.0,slices,,6.0,5.0,https://pypi.org/project/slices,2025-03-01 10:21:03.000,1.0,337.0,358.0,,,,2.0,,xiaohang07/slices,https://hub.docker.com/r/xiaohang07/slices,2025-03-01 10:48:02.812954,1.0,572.0,,,,,,,,,,,,,,, +181,Bgolearn,,active-learning,https://bgolearn.netlify.app,[Materials & Design 2024 | NPJ com mat 2024] A Bayesian global optimization package for material design Adaptive..,13,True,MIT,"['materials-discovery', 'probabilistic']",Bin-Cao/Bgolearn,https://github.com/Bin-Cao/Bgolearn,2022-07-10 07:25:48,2025-06-19 10:34:22.000000,2025-06-19 10:34:22,236.0,2.0,15.0,5.0,1.0,1.0,2.0,94.0,2025-02-23 02:11:31.000,2.3.8,44.0,3.0,Bgolearn,,,,https://pypi.org/project/Bgolearn,2025-02-23 02:11:31.000,,227.0,229.0,,,,3.0,,,,,,,57.0,,,,,,,,,,,,,, +182,Librascal,,rep-eng,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,13,False,LGPL-2.1,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2024-01-11 17:38:31.000000,2023-11-30 14:48:28,2931.0,,20.0,21.0,201.0,115.0,132.0,81.0,,,3.0,30.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +183,AtomGPT,,language-models,https://github.com/usnistgov/atomgpt,"This repository is no longer maintained. For the latest updates and continued development, please visit:..",13,True,https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst,"['generative', 'pretrained', 'transformer']",usnistgov/atomgpt,https://github.com/usnistgov/atomgpt,2023-07-17 02:20:53,2025-06-27 04:27:20.000000,2025-06-27 04:27:20,197.0,30.0,15.0,2.0,20.0,2.0,,67.0,2025-03-22 18:00:02.000,2024.11.30,4.0,6.0,atomgpt,,1.0,,https://pypi.org/project/atomgpt,2025-03-22 17:58:23.000,1.0,122.0,122.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +184,aviary,,materials-discovery,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,13,True,MIT,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2025-04-19 20:55:02.000000,2025-04-19 20:55:01,655.0,4.0,13.0,3.0,72.0,4.0,29.0,57.0,2025-03-29 02:34:40.000,1.1.2,7.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +185,pair_allegro,,md,https://github.com/mir-group/pair_nequip_allegro,LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials.,13,True,MIT,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_nequip_allegro,2021-08-09 17:26:51,2025-05-28 14:50:12.000000,2025-05-16 15:52:03,125.0,6.0,8.0,11.0,6.0,7.0,34.0,45.0,2025-05-03 01:13:32.000,0.7.0,2.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,mir-group/pair_nequip_allegro,,,,,,,,,,,,, +186,mat_discover,,unsupervised,https://github.com/sparks-baird/mat_discover,A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.,13,True,MIT,"['materials-discovery', 'rep-eng', 'htc']",sparks-baird/mat_discover,https://github.com/sparks-baird/mat_discover,2021-08-05 04:07:17,2024-08-20 20:53:44.000000,2024-08-20 20:53:44,1538.0,,9.0,1.0,127.0,29.0,11.0,44.0,2023-06-23 13:54:55.000,2.2.11,33.0,5.0,mat_discover,,,,https://pypi.org/project/mat_discover,2023-06-23 13:54:00.492,,387.0,387.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +187,SALTED,,ml-dft,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response).,13,True,GPL-3.0,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2025-06-04 08:10:10.000000,2025-06-04 08:09:57,784.0,4.0,5.0,4.0,54.0,2.0,6.0,39.0,2025-03-11 16:23:45.000,3.0.2,4.0,24.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +188,xtal2png,,rep-learn,https://github.com/sparks-baird/xtal2png,Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning..,13,False,MIT,['computer-vision'],sparks-baird/xtal2png,https://github.com/sparks-baird/xtal2png,2022-05-20 19:01:34,2023-10-04 00:53:43.000000,2023-07-01 05:57:28,771.0,,3.0,3.0,148.0,26.0,39.0,37.0,2023-02-04 01:27:08.864,0.6.1,24.0,6.0,xtal2png,,,,https://pypi.org/project/xtal2png,2022-06-23 04:45:54.000,,64.0,64.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +189,pySIPFENN,,rep-eng,https://github.com/PhasesResearchLab/pySIPFENN,Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique..,13,True,LGPL-3.0,"['material-defect', 'defects-disorder', 'pretrained', 'transfer-learning']",PhasesResearchLab/pySIPFENN,https://github.com/PhasesResearchLab/pySIPFENN,2022-10-14 19:13:09,2025-04-25 13:31:12.000000,2025-04-25 13:31:11,526.0,3.0,5.0,4.0,17.0,4.0,2.0,24.0,2025-03-06 17:15:11.000,0.16.3,17.0,4.0,pysipfenn,conda-forge/pysipfenn,7.0,7.0,https://pypi.org/project/pysipfenn,2025-03-06 17:16:12.000,,95.0,669.0,https://anaconda.org/conda-forge/pysipfenn,2025-04-22 14:58:37.989,16005.0,2.0,,,,,,,107.0,,,,,,,,,,,,,, +190,Deep Learning for Molecules and Materials Book,,educational,https://dmol.pub/,Deep learning for molecules and materials book.,12,False,https://github.com/whitead/dmol-book/blob/main/LICENSE,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2025-07-02 16:15:12.000000,2023-07-02 18:02:56,558.0,,116.0,14.0,92.0,31.0,130.0,656.0,,,,19.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +191,mat2vec,,language-models,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,False,MIT,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000000,2023-05-06 22:45:49,55.0,,178.0,39.0,8.0,6.0,18.0,628.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +192,DeepLearningLifeSciences,,educational,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,12,False,MIT,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000000,2021-09-17 05:10:37,52.0,,153.0,25.0,15.0,11.0,10.0,373.0,2019-10-28 18:46:28.000,1.0,1.0,9.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +193,SISSO,,rep-eng,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,12,True,Apache-2.0,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2025-03-21 06:19:29.000000,2025-03-21 06:19:29,203.0,,86.0,7.0,4.0,18.0,59.0,285.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +194,DeepH-pack,,ml-dft,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,12,True,LGPL-3.0,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-10-07 10:24:16.000000,2024-10-07 10:24:16,68.0,,50.0,6.0,18.0,24.0,41.0,284.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +195,TensorMol,,ml-iap,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,GPL-3.0,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000000,2018-03-30 12:26:14,1724.0,,74.0,45.0,8.0,18.0,19.0,274.0,2017-11-08 18:05:50.000,0.1,1.0,12.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +196,gptchem,,language-models,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,12,False,MIT,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11.000000,2023-10-04 11:27:09,147.0,,43.0,8.0,5.0,21.0,2.0,251.0,2023-11-30 09:31:51.000,0.0.4,4.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,2023-10-04 11:28:07.000,,34.0,34.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +197,ANI-1,,ml-iap,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,False,MIT,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2024-03-11 21:50:26.000000,2024-03-11 21:50:26,112.0,,54.0,32.0,9.0,16.0,21.0,224.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +198,DMFF,,md,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,12,True,LGPL-3.0,['lang-cpp'],deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2025-04-10 05:17:06.000000,2025-04-10 05:17:05,434.0,3.0,46.0,9.0,167.0,11.0,17.0,175.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +199,Pacemaker,,ml-iap,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,12,True,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-11-20 09:47:10.000000,2024-11-20 09:47:10,173.0,,21.0,5.0,27.0,20.0,40.0,87.0,2022-10-24 21:50:17.233,0.2.8,2.0,7.0,python-ace,,,,https://pypi.org/project/python-ace,2022-10-24 21:50:17.233,,9.0,9.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +200,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,https://github.com/NREL/nfp/blob/master/LICENSE,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2024-02-24 20:11:49.000000,2022-06-14 22:18:28,143.0,,34.0,6.0,19.0,2.0,6.0,60.0,2022-04-27 17:05:25.000,0.3.12,13.0,4.0,,,16.0,16.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +201,wfl,,ml-iap,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,12,True,GPL-2.0,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2025-02-21 16:18:01.000000,2025-02-21 16:17:46,1231.0,,19.0,8.0,191.0,67.0,96.0,40.0,2024-04-25 15:07:11.000,0.2.4,4.0,19.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +202,flare++,,active-learning,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,12,False,MIT,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000000,2022-02-24 19:00:50,989.0,,7.0,6.0,29.0,8.0,17.0,36.0,2021-12-23 05:02:12.000,0.1.1,25.0,10.0,flare_pp,,2.0,,https://pypi.org/project/flare_pp,2021-12-23 05:02:12.000,2.0,70.0,70.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +203,OpenKIM,,datasets,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",12,True,LGPL-2.1,"['model-repository', 'knowledge-base', 'pretrained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2025-05-26 01:39:13.000000,2025-04-29 14:39:02,2414.0,5.0,20.0,9.0,59.0,15.0,22.0,32.0,,,,27.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +204,GlassPy,,rep-eng,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,12,True,GPL-3.0,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-10-13 22:55:06.000000,2024-10-13 21:52:07,374.0,,7.0,5.0,15.0,7.0,8.0,32.0,2024-10-13 22:55:06.000,0.5.3,15.0,2.0,glasspy,,7.0,7.0,https://pypi.org/project/glasspy,2024-09-05 19:43:43.000,,242.0,242.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +205,CBFV,,rep-eng,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,12,False,,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000000,2021-10-24 17:10:17,49.0,,5.0,3.0,7.0,5.0,5.0,27.0,2021-10-24 17:22:06.000,1.1.0,3.0,3.0,CBFV,,21.0,21.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,405.0,405.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +206,GElib,,math,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,12,True,MPL-2.0,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2025-07-03 16:56:32.000000,2025-07-03 16:56:32,700.0,54.0,3.0,4.0,8.0,4.0,4.0,25.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +207,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,12,True,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,,,,2021-04-23 16:12:56,2025-05-23 20:35:49.000000,,,,4.0,,,9.0,90.0,14.0,2024-07-26 09:35:09.000,2.3.1,16.0,,calorine,,7.0,,https://pypi.org/project/calorine,2025-05-23 20:35:49.000,7.0,1240.0,1240.0,,,,2.0,,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,, +208,GNoME Explorer,,community,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,11,True,Apache-2.0,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2025-06-19 02:10:35.000000,2025-03-03 17:49:03,13.0,,160.0,47.0,10.0,21.0,4.0,1008.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +209,Materials Discovery: GNoME,,materials-discovery,https://github.com/google-deepmind/materials_discovery,"Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials.",11,True,Apache-2.0,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2025-06-19 02:10:35.000000,2025-03-03 17:49:03,13.0,,160.0,47.0,10.0,21.0,4.0,1008.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +210,Geometric GNN Dojo,,educational,https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb,"New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.",11,False,MIT,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2024-05-22 11:06:03.000000,2023-06-18 23:17:32,26.0,,46.0,9.0,4.0,3.0,6.0,500.0,2023-06-18 23:20:44.000,0.2.0,2.0,2.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +211,ReLeaSE,,reinforcement-learning,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,MIT,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000000,2021-12-08 19:49:36,160.0,,134.0,18.0,9.0,27.0,8.0,362.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +212,Introduction to AI-driven Science on Supercomputers: A Student Training Series,,educational,https://github.com/argonne-lcf/ai-science-training-series,,11,False,,"['general-ml', 'rep-learn', 'language-models']",argonne-lcf/ai-science-training-series,https://github.com/argonne-lcf/ai-science-training-series,2021-09-21 15:41:56,2024-11-15 20:50:18.000000,2024-11-15 20:50:16,758.0,,633.0,30.0,44.0,1.0,2.0,223.0,2024-01-25 21:00:11.000,2022Series,2.0,36.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +213,nablaDFT,,datasets,https://github.com/AIRI-Institute/nablaDFT,nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset.,11,True,MIT,"['ml-dft', 'ml-wft', 'drug-discovery', 'ml-iap', 'benchmarking']",AIRI-Institute/nablaDFT,https://github.com/AIRI-Institute/nablaDFT,2022-08-18 15:46:09,2025-03-20 15:08:30.000000,2025-02-11 07:53:39,386.0,,24.0,4.0,29.0,6.0,18.0,212.0,2024-06-11 18:47:16.000,1.0,1.0,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +214,AI4Chemistry course,,educational,https://github.com/schwallergroup/ai4chem_course,"EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course.",11,True,MIT,['chemistry'],schwallergroup/ai4chem_course,https://github.com/schwallergroup/ai4chem_course,2022-08-22 07:29:30,2025-04-30 11:07:04.000000,2025-04-30 11:07:00,249.0,8.0,48.0,5.0,12.0,1.0,3.0,192.0,,,,7.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +215,SPICE,,datasets,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,11,True,MIT,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2025-02-18 18:27:38.000000,2025-02-18 18:27:38,45.0,,9.0,18.0,49.0,19.0,54.0,175.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,7.0,,,,2.0,,,,,,,287.0,,,,,,,,,,,,,, +216,Neural-Network-Models-for-Chemistry,,community,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,11,True,MIT,['rep-learn'],Eipgen/Neural-Network-Models-for-Chemistry,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,2022-05-23 06:35:09,2025-06-23 12:43:35.000000,2025-06-23 12:43:35,259.0,6.0,20.0,6.0,24.0,1.0,1.0,148.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +217,ASAP,,unsupervised,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,11,False,MIT,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2024-06-27 12:53:17.000000,2024-06-27 12:53:00,763.0,,29.0,6.0,38.0,7.0,19.0,148.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,8.0,8.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +218,PiNN,,ml-iap,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,BSD-3-Clause,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2025-04-16 16:33:42.000000,2025-02-17 13:34:18,187.0,,35.0,5.0,30.0,1.0,6.0,115.0,2019-10-09 09:21:30.000,0.3.0,1.0,6.0,,,,,,,,,6.0,,,,2.0,,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2025-02-17 13:47:01.387257,,475.0,,,,,,,,,,,,,,, +219,jarvis-tools-notebooks,,educational,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,11,True,NIST,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2025-06-23 17:20:40.000000,2025-06-23 17:20:40,872.0,12.0,35.0,3.0,52.0,1.0,,88.0,,,,6.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +220,ChatMOF,,language-models,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,11,True,MIT,['generative'],Yeonghun1675/ChatMOF,https://github.com/Yeonghun1675/ChatMOF,2023-05-19 06:33:06,2025-05-15 23:22:17.000000,2025-05-15 23:22:12,75.0,1.0,18.0,1.0,12.0,,8.0,85.0,2024-06-14 09:56:27.000,0.2.1,17.0,2.0,chatmof,,3.0,3.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,201.0,201.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +221,AMPtorch,,general-tool,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,11,False,GPL-3.0,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000000,2023-07-16 02:08:13,759.0,,32.0,8.0,99.0,7.0,26.0,60.0,2023-07-16 02:11:38.000,1.0,3.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +222,SchNetPack G-SchNet,,generative,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,11,True,MIT,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-11-07 17:18:59.000000,2024-11-07 17:18:59,173.0,,11.0,3.0,1.0,,17.0,59.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +223,SIMPLE-NN,,ml-iap,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,GPL-3.0,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000000,2022-01-27 05:04:05,586.0,,19.0,11.0,91.0,4.0,26.0,48.0,2021-09-23 01:41:42.000,1.1.1,9.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +224,nlcc,,language-models,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,False,MIT,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000000,2023-02-04 03:06:33,144.0,,7.0,4.0,1.0,,9.0,45.0,2023-02-04 03:11:01.949,0.6.0,10.0,3.0,nlcc,,2.0,2.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,39.0,39.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +225,PDynA,,rep-eng,https://github.com/WMD-group/PDynA,Python package to analyse the structural dynamics of perovskites.,11,True,MIT,['md'],WMD-group/PDynA,https://github.com/WMD-group/PDynA,2022-11-21 11:58:42,2024-12-13 19:47:05.000000,2024-10-11 23:48:13,238.0,,3.0,2.0,6.0,,3.0,43.0,2024-09-23 21:29:33.000,1.1.1,3.0,4.0,pdyna,,2.0,2.0,https://pypi.org/project/pdyna,2024-09-23 21:29:33.000,,36.0,36.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +226,pumml,,unsupervised,https://github.com/ncfrey/pumml,Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to..,11,False,MIT,['materials-discovery'],ncfrey/pumml,https://github.com/ncfrey/pumml,2019-07-08 17:57:19,2024-02-21 18:04:36.000000,2021-03-12 18:28:56,81.0,,13.0,4.0,15.0,,3.0,36.0,2020-11-16 18:30:10.000,0.0.2,2.0,3.0,pumml,,2.0,2.0,https://pypi.org/project/pumml,2020-11-16 18:30:10.000,,23.0,23.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +227,FAENet,,rep-learn,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,11,False,MIT,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2025-02-12 17:26:03.000000,2023-10-12 08:46:22,125.0,,2.0,3.0,6.0,,,34.0,2023-09-12 04:00:49.000,0.1.2,3.0,3.0,faenet,,3.0,3.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,63.0,63.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +228,cmlkit,,rep-eng,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,False,MIT,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,33.0,,,25.0,1.0,cmlkit,,7.0,6.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,192.0,192.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +229,MPDS API,,datasets,https://github.com/mpds-io/mpds-api,"Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials..",11,True,CC-BY-4.0,['phase-transition'],mpds-io/mpds-api,https://github.com/mpds-io/mpds-api,2016-11-21 14:17:47,2025-05-24 13:49:17.000000,2025-05-24 13:49:16,188.0,2.0,5.0,2.0,37.0,9.0,17.0,27.0,2019-12-21 21:49:29.000,0.0.22,15.0,5.0,mpds_client,,,,https://pypi.org/project/mpds_client,2020-09-14 18:29:16.000,,238.0,238.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +230,COSMO Software Cookbook,,educational,https://github.com/lab-cosmo/atomistic-cookbook,A collection of simulation recipes for the atomic-scale modeling of materials and molecules.,11,True,BSD-3-Clause,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2025-07-03 16:45:28.000000,2025-07-02 18:28:35,177.0,40.0,4.0,16.0,142.0,4.0,16.0,24.0,,,,15.0,,,,,,,,,,,,,1.0,,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,,, +231,BOSS,,materials-discovery,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,11,True,Apache-2.0,['probabilistic'],,,2020-02-12 08:48:33,2024-11-13 14:59:24.000000,,,,11.0,,,2.0,30.0,23.0,2024-10-09 15:57:12.000,1.12.0,51.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-11-13 14:59:24.000,,415.0,415.0,,,,2.0,,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,, +232,SiMGen,,generative,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,11,True,MIT,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2025-06-02 07:33:48.000000,2025-04-27 22:52:24,310.0,3.0,3.0,2.0,30.0,1.0,3.0,20.0,2025-04-27 22:50:06.000,0.3,6.0,4.0,simgen,,2.0,2.0,https://pypi.org/project/simgen,2024-12-13 17:14:45.000,,25.0,25.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +233,BenchML,,rep-eng,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,11,False,Apache-2.0,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000000,2023-05-24 15:04:57,341.0,,6.0,5.0,9.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,57.0,57.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +234,pretrained-gnns,,rep-learn,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,False,MIT,['pretrained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39.000000,2023-07-29 06:21:39,13.0,,166.0,17.0,8.0,35.0,29.0,1020.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +235,OpenChem,,general-tool,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,10,False,MIT,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000000,2022-04-27 19:27:40,444.0,,117.0,36.0,12.0,15.0,2.0,715.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +236,Awesome-Scientific-Language-Models,,community,https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models,A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (EMNLP24).,10,True,MIT,"['language-models', 'general-ml', 'pretrained', 'multimodal']",yuzhimanhua/Awesome-Scientific-Language-Models,https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models,2024-01-05 03:32:57,2025-06-21 11:59:38.000000,2025-06-21 11:59:31,96.0,2.0,32.0,10.0,10.0,,,591.0,,,,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +237,MoLFormers UI,,community,https://molformer.res.ibm.com/,A family of foundation models trained on chemicals.,10,False,Apache-2.0,"['transformer', 'language-models', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2025-05-08 02:54:29.000000,2023-10-16 16:33:13,7.0,,51.0,10.0,4.0,11.0,10.0,322.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +238,MoLFormer,,language-models,https://github.com/IBM/molformer,Repository for MolFormer.,10,False,Apache-2.0,"['transformer', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2025-05-08 02:54:29.000000,2023-10-16 16:33:13,7.0,,51.0,10.0,4.0,11.0,10.0,322.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +239,GDC,,rep-learn,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,False,MIT,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000000,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,,11.0,272.0,,,,3.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +240,Awesome Materials & Chemistry Datasets,,community,https://github.com/blaiszik/awesome-matchem-datasets,A curated list of the most useful datasets in materials science and chemistry for training machine learning and AI..,10,True,MIT,"['datasets', 'experimental-data', 'literature-data', 'proprietary']",blaiszik/awesome-matchem-datasets,https://github.com/blaiszik/awesome-matchem-datasets,2025-04-02 03:47:04,2025-06-27 20:32:12.000000,2025-06-27 20:32:12,57.0,39.0,22.0,10.0,9.0,5.0,2.0,178.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +241,Grad DFT,,ml-dft,https://github.com/XanaduAI/GradDFT,GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,10,False,Apache-2.0,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2024-02-13 16:05:53.000000,2024-02-13 16:05:51,419.0,,8.0,5.0,44.0,11.0,43.0,101.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +242,NIST ChemNLP,,language-models,https://github.com/usnistgov/chemnlp,ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data.,10,True,MIT,['literature-data'],usnistgov/chemnlp,https://github.com/usnistgov/chemnlp,2022-08-10 11:43:44,2025-06-27 04:31:24.000000,2025-06-27 04:31:24,82.0,1.0,20.0,7.0,15.0,2.0,,76.0,2023-08-07 12:49:57.000,2023.7.1,6.0,2.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,71.0,71.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +243,GRACE,,uip,https://github.com/ICAMS/grace-tensorpotential,GRACE models and gracemaker (as implemented in TensorPotential package).,10,True,https://github.com/ICAMS/grace-tensorpotential/blob/master/LICENSE.md,"['ml-iap', 'pretrained', 'md', 'rep-learn', 'rep-eng']",ICAMS/grace-tensorpotential,https://github.com/ICAMS/grace-tensorpotential,2024-09-12 10:21:02,2025-06-24 09:30:41.000000,2025-06-24 09:30:41,71.0,2.0,4.0,1.0,3.0,3.0,3.0,61.0,2025-03-10 13:34:15.000,0.5.1,6.0,3.0,,,6.0,6.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +244,pair_nequip,,md,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,10,True,MIT,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2025-04-25 20:00:21.000000,2025-04-25 20:00:21,102.0,1.0,13.0,8.0,8.0,13.0,20.0,44.0,2025-04-23 19:04:15.000,0.6.0,5.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +245,Atom2Vec,,rep-learn,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,10,False,MIT,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2024-02-23 21:44:03.000000,2024-02-23 21:43:58,4.0,,9.0,1.0,1.0,3.0,1.0,37.0,2024-02-23 21:43:41.000,1.1.0,2.0,1.0,atom2vec,,5.0,5.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,27.0,27.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +246,NeuralXC,,ml-dft,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,BSD-3-Clause,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40.000000,2021-07-05 21:36:23,337.0,,10.0,4.0,10.0,5.0,5.0,35.0,,,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +247,Atomvision,,visualization,https://github.com/usnistgov/atomvision,Deep learning framework for atomistic image data.,10,True,https://github.com/usnistgov/atomvision/blob/master/LICENSE.md,"['computer-vision', 'experimental-data', 'rep-learn']",usnistgov/atomvision,https://github.com/usnistgov/atomvision,2021-09-16 20:33:46,2025-06-27 04:28:30.000000,2025-06-27 04:28:29,124.0,1.0,17.0,8.0,15.0,4.0,4.0,35.0,2023-05-08 03:15:44.402,2023.5.6,6.0,3.0,atomvision,,,,https://pypi.org/project/atomvision,2023-05-08 03:15:44.402,,66.0,66.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +248,Point Edge Transformer (PET),,ml-iap,https://github.com/spozdn/pet,Point Edge Transformer.,10,True,MIT,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2025-05-31 20:07:47.000000,2025-03-18 17:31:14,258.0,,7.0,3.0,14.0,5.0,,29.0,,,,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +249,OBELiX,,datasets,https://github.com/NRC-Mila/OBELiX,A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State..,10,True,CC-BY-4.0,"['experimental-data', 'transport-phenomena']",NRC-Mila/OBELiX,https://github.com/NRC-Mila/OBELiX,2025-02-07 20:00:55,2025-05-16 06:58:50.000000,2025-05-16 06:58:36,134.0,12.0,4.0,2.0,6.0,1.0,1.0,24.0,2025-05-16 06:58:50.000,1.2.0,5.0,5.0,obelix-data,,,,https://pypi.org/project/obelix-data,2025-05-16 06:58:50.000,,63.0,63.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +250,AGOX,,materials-discovery,https://agox.gitlab.io/agox/,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,10,True,GPL-3.0,['structure-optimization'],,,2022-03-08 09:08:13,2025-06-24 20:17:10.000000,,,,8.0,,,10.0,18.0,15.0,2025-06-24 17:36:04.000,3.10.1,11.0,,agox,,1.0,,https://pypi.org/project/agox,2025-06-24 20:17:10.000,1.0,334.0,334.0,,,,3.0,,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,, +251,CCS_fit,,ml-iap,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,10,False,GPL-3.0,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2025-02-03 19:31:44.000000,2024-02-16 09:31:25,762.0,,11.0,2.0,13.0,8.0,6.0,10.0,2024-02-16 09:31:34.000,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,2024-02-16 09:31:34.000,,384.0,410.0,,,,2.0,,,,,,,843.0,,,,,,,,,,,,,, +252,DeepModeling Projects,,community,https://github.com/deepmodeling/deepmodeling-projects,DeepModeling projects.,10,True,CC-BY-4.0,,deepmodeling/deepmodeling-projects,https://github.com/deepmodeling/deepmodeling-projects,2023-10-31 02:20:45,2025-07-03 18:04:46.000000,2025-06-27 04:08:01,193.0,16.0,2.0,3.0,102.0,,,7.0,2025-06-27 04:08:11.000,2025.06.26,83.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +253,aiida-mlip,,ml-iap,https://github.com/ElliottKasoar/aiida-mlip,machine learning interatomic potentials aiida plugin.,10,False,BSD-3-Clause,"['workflows', 'structure-optimization', 'md']",ElliottKasoar/aiida-mlip,https://github.com/ElliottKasoar/aiida-mlip,2024-02-05 10:36:44,2025-06-23 00:56:41.000000,2025-05-21 12:08:45,75.0,3.0,,,2.0,,,1.0,2024-05-30 09:44:44.000,0.2.0,6.0,5.0,aiida-mlip,,,,https://pypi.org/project/aiida-mlip,2024-06-10 09:21:50.000,,166.0,166.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +254,SE(3)-Transformers,,rep-learn,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,MIT,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000000,2021-11-18 09:11:56,63.0,,71.0,17.0,5.0,11.0,17.0,528.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +255,EDM,,generative,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,MIT,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000000,2022-07-10 17:56:12,6.0,,123.0,8.0,,3.0,37.0,512.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +256,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,False,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000000,2023-10-03 09:57:19,103.0,,65.0,3.0,,1.0,31.0,324.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +257,SchNet,,ml-iap,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,MIT,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000000,2018-09-04 08:42:34,53.0,,69.0,15.0,,1.0,2.0,249.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +258,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,False,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000000,2023-04-26 14:20:12,36.0,,30.0,3.0,2.0,1.0,14.0,202.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +259,MolSkill,,language-models,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,9,False,MIT,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2025-03-25 16:28:35.902000,2023-10-31 17:03:36,81.0,,11.0,5.0,8.0,2.0,4.0,107.0,2023-08-04 12:22:15.000,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,,,14.0,https://anaconda.org/msr-ai4science/molskill,2025-03-25 16:28:35.902,410.0,3.0,,,,,,,,,,,,,,,,,,,,, +260,MoleculeNet Leaderboard,,datasets,https://github.com/deepchem/moleculenet,,9,False,MIT,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000000,2021-04-29 19:51:06,78.0,,22.0,4.0,15.0,24.0,5.0,101.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +261,tinker-hp,,ml-iap,https://github.com/TinkerTools/tinker-hp,Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs.,9,True,https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2025-06-23 14:06:33.000000,2025-06-23 14:04:13,576.0,1.0,23.0,13.0,2.0,5.0,20.0,92.0,2019-11-24 16:21:50.000,published-version-V1,1.0,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +262,GATGNN: Global Attention Graph Neural Network,,rep-learn,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,True,MIT,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2024-12-17 04:07:15.000000,2024-12-17 04:07:14,100.0,,17.0,7.0,,4.0,3.0,80.0,2021-04-05 06:49:29.000,0.2,2.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +263,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2024-12-17 23:48:29.000000,2024-12-17 23:46:28,917.0,,14.0,7.0,66.0,24.0,58.0,65.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +264,PROPhet,,ml-dft,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,GPL-3.0,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000000,2018-04-19 02:00:46,120.0,,26.0,13.0,6.0,9.0,7.0,65.0,2018-04-15 16:55:15.000,1.2,3.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +265,Finetuna,,active-learning,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,9,False,MIT,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2024-05-15 17:26:24.000000,2024-05-15 17:25:23,1200.0,,13.0,1.0,40.0,5.0,15.0,55.0,,,,11.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +266,DeepMD-GNN,,ml-iap,https://github.com/deepmodeling/deepmd-gnn,DeePMD-kit plugin for various graph neural network models.,9,True,LGPL-3.0,"['rep-learn', 'md', 'uip', 'lang-cpp']",deepmodeling/deepmd-gnn,https://github.com/deepmodeling/deepmd-gnn,2024-08-21 21:01:06,2025-06-30 19:32:47.000000,2025-06-16 16:01:26,56.0,8.0,6.0,1.0,61.0,5.0,1.0,47.0,2025-03-05 10:58:22.000,0.1.1,2.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +267,lie-nn,,math,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,False,MIT,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34.000000,2023-06-20 22:30:53,249.0,,2.0,7.0,3.0,1.0,,34.0,2023-06-20 22:31:12.000,0.0.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +268,iam-notebooks,,educational,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,Apache-2.0,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2025-04-14 13:36:11.000000,2025-01-07 15:02:36,245.0,,5.0,4.0,8.0,6.0,,28.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +269,SkipAtom,,rep-eng,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",9,False,MIT,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2025-04-22 14:58:21.947000,2022-05-04 13:18:30,46.0,,3.0,1.0,7.0,3.0,1.0,26.0,2022-05-04 13:20:18.000,1.2.5,12.0,1.0,skipatom,conda-forge/skipatom,4.0,4.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,18.0,87.0,https://anaconda.org/conda-forge/skipatom,2025-04-22 14:58:21.947,2484.0,3.0,,,,,,,,,,,,,,,,,,,,, +270,ACE1.jl,,ml-iap,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,9,True,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2025-04-15 15:02:03.000000,2025-04-15 15:02:03,564.0,3.0,7.0,5.0,32.0,22.0,24.0,22.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +271,Q-stack,,ml-dft,https://github.com/lcmd-epfl/Q-stack,Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).,9,True,MIT,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2025-06-11 12:28:38.000000,2025-06-11 11:45:43,451.0,5.0,5.0,1.0,55.0,10.0,24.0,18.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +272,ACEhamiltonians,,ml-dft,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",9,False,MIT,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-12-02 16:55:05.000000,2023-04-12 15:04:14,33.0,,7.0,5.0,42.0,2.0,3.0,16.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +273,OPTIMADE Tutorial Exercises,,educational,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,False,MIT,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31.000000,2023-09-27 08:32:30,49.0,,7.0,11.0,15.0,,3.0,15.0,2023-06-12 07:47:14.000,2.0.1,5.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +274,AIS Square,,datasets,https://github.com/deepmodeling/AIS-Square,"A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the..",9,True,LGPL-3.0,"['community', 'model-repository']",deepmodeling/AIS-Square,https://github.com/deepmodeling/AIS-Square,2022-09-13 09:52:30,2025-06-30 16:12:53.000000,2025-06-30 16:12:52,1062.0,108.0,8.0,8.0,210.0,5.0,1.0,13.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +275,NNsforMD,,ml-iap,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",9,False,MIT,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000000,2022-11-10 13:04:45,265.0,,6.0,2.0,,,,11.0,2022-04-12 15:15:00.183,2.0.0,5.0,2.0,pyNNsMD,,2.0,2.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,25.0,25.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +276,Materials Data Facility (MDF),,datasets,https://www.materialsdatafacility.org,"A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,..",9,False,Apache-2.0,,materials-data-facility/connect_client,https://github.com/materials-data-facility/connect_client,2018-09-12 20:49:58,2024-03-10 03:11:45.000000,2024-02-05 22:48:40,158.0,,1.0,3.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58.000,0.5.0,23.0,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +277,dftio,,ml-dft,https://github.com/deepmodeling/dftio,dftio is to assist machine learning communities to transcript DFT output into a format that is easy to read or used by..,9,True,LGPL-3.0,"['data-structures', 'workflows']",deepmodeling/dftio,https://github.com/deepmodeling/dftio,2024-05-27 14:03:49,2025-05-26 11:12:41.000000,2025-05-26 11:12:41,115.0,12.0,4.0,3.0,7.0,1.0,3.0,9.0,2024-06-03 03:30:43.000,0.0.1,1.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +278,2DMD dataset,,datasets,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,False,Apache-2.0,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,7.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +279,ACEfit,,ml-iap,https://github.com/ACEsuit/ACEfit.jl,,9,True,MIT,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2024-09-14 11:29:30.000000,2024-09-14 11:17:37,266.0,,8.0,4.0,31.0,22.0,35.0,7.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +280,ai4material_design,,rep-learn,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,False,Apache-2.0,"['pretrained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,7.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +281,Awesome Neural Geometry,,community,https://github.com/neurreps/awesome-neural-geometry,"A curated collection of resources and research related to the geometry of representations in the brain, deep networks,..",8,True,,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2025-02-18 18:26:50.000000,2025-02-18 18:26:50,128.0,,65.0,31.0,14.0,,1.0,984.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +282,Awesome-Graph-Generation,,community,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,8,True,,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2025-01-04 01:33:47.000000,2025-01-04 01:33:47,86.0,,22.0,9.0,2.0,1.0,,342.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +283,molecularGNN_smiles,,rep-learn,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",8,False,Apache-2.0,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000000,2020-11-28 02:04:45,79.0,,79.0,4.0,,6.0,1.0,325.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +284,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,False,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000000,2023-03-19 13:36:55,68.0,,77.0,15.0,7.0,5.0,1.0,290.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +285,EquiformerV2,,ml-iap,https://github.com/atomicarchitects/equiformer_v2,[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,8,True,MIT,"['pretrained', 'uip', 'rep-learn']",atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2025-02-11 15:58:33.000000,2025-02-11 15:58:18,17.0,,37.0,4.0,1.0,16.0,8.0,272.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +286,Equiformer,,rep-learn,https://github.com/atomicarchitects/equiformer,[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,8,True,MIT,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2025-02-11 19:52:36.000000,2025-02-11 19:52:27,8.0,,46.0,5.0,2.0,10.0,11.0,240.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +287,QDF for molecule,,ml-esm,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",8,False,MIT,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000000,2021-02-20 03:46:09,20.0,,47.0,3.0,,1.0,3.0,220.0,,,,1.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +288,BestPractices,,educational,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,8,False,MIT,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000000,2023-11-17 02:58:25,17.0,,71.0,7.0,8.0,5.0,2.0,186.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +289,G-SchNet,,generative,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,False,MIT,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000000,2023-03-24 12:05:41,64.0,,25.0,5.0,,,10.0,137.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +290,Awesome Neural SBI,,community,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,8,True,MIT,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2025-05-17 00:27:34.000000,2025-05-17 00:27:34,63.0,4.0,9.0,6.0,5.0,1.0,1.0,118.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +291,AI for Science paper collection,,community,https://github.com/AI4QC/AI_for_Science_paper_collection,List the AI for Science papers accepted by top conferences.,8,True,Apache-2.0,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/AI4QC/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-09-14 16:58:10.000000,2024-09-14 16:58:10,79.0,,12.0,3.0,10.0,1.0,,117.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,AI4QC/AI_for_Science_paper_collection,,,,,,,,,,,,, +292,DeePKS-kit,,ml-dft,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,8,True,LGPL-3.0,['ml-functional'],deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2025-04-28 21:22:59.000000,2025-04-28 21:22:58,385.0,1.0,36.0,13.0,47.0,13.0,17.0,109.0,,,,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +293,AIMNet,,ml-iap,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,8,False,MIT,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000000,2019-11-21 23:49:00,7.0,,27.0,9.0,2.0,5.0,,105.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +294,HamGNN,,ml-dft,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,8,True,GPL-3.0,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2025-06-09 06:42:30.000000,2025-06-09 06:19:06,106.0,10.0,20.0,5.0,1.0,44.0,8.0,104.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +295,ANI-1 Dataset,,datasets,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,MIT,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000000,2022-08-08 15:56:17,25.0,,18.0,11.0,2.0,8.0,3.0,97.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +296,graphite,,rep-learn,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,8,True,MIT,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2024-08-08 04:10:45.000000,2024-08-08 04:10:44,30.0,,13.0,3.0,4.0,3.0,1.0,85.0,,,,2.0,,,15.0,15.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +297,LLaMP,,language-models,https://github.com/chiang-yuan/llamp,A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An..,8,True,BSD-3-Clause,"['multimodal', 'RAG', 'materials-discovery', 'pretrained', 'lang-js', 'lang-py']",chiang-yuan/llamp,https://github.com/chiang-yuan/llamp,2023-07-01 08:15:34,2024-10-14 03:45:00.000000,2024-10-14 03:44:53,375.0,,13.0,1.0,30.0,8.0,17.0,84.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +298,JaQMC,,ml-wft,https://github.com/bytedance/jaqmc,JAX accelerated Quantum Monte Carlo.,8,True,Apache-2.0,,bytedance/jaqmc,https://github.com/bytedance/jaqmc,2022-11-01 11:22:09,2025-05-30 08:54:08.000000,2025-05-30 08:54:08,14.0,3.0,10.0,6.0,9.0,,5.0,81.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +299,MACE-Jax,,ml-iap,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,8,False,MIT,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2025-04-25 06:15:51.000000,2023-10-04 08:07:35,207.0,,12.0,10.0,2.0,3.0,5.0,73.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +300,LapJAX,,math,https://github.com/YWolfeee/lapjax,"A JAX based package designed for efficient second order operators (e.g., laplacian) computation.",8,False,MIT,,YWolfeee/lapjax,https://github.com/YWolfeee/lapjax,2023-09-22 18:58:06,2024-03-15 03:58:10.000000,2024-03-15 03:58:10,94.0,,10.0,2.0,20.0,,4.0,72.0,2023-10-31 08:56:45.000,0.0.1,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +301,PyNEP,,ml-iap,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,8,True,MIT,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-12-15 08:31:24.000000,2024-12-15 08:31:24,95.0,,17.0,2.0,18.0,5.0,8.0,54.0,2024-10-19 05:28:19.000,1.0.0,1.0,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +302,DSECOP,,educational,https://github.com/GDS-Education-Community-of-Practice/DSECOP,This repository contains data science educational materials developed by DSECOP Fellows.,8,True,CCO-1.0,,GDS-Education-Community-of-Practice/DSECOP,https://github.com/GDS-Education-Community-of-Practice/DSECOP,2022-03-07 17:47:33,2025-04-29 14:02:34.000000,2025-04-29 14:02:34,557.0,2.0,26.0,10.0,26.0,1.0,7.0,49.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +303,Sketchmap,,unsupervised,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,False,GPL-3.0,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-09-30 15:56:54.000000,2023-05-24 22:47:50,64.0,,10.0,29.0,1.0,4.0,5.0,46.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +304,CHIPS-FF,,uip,https://github.com/usnistgov/chipsff,Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093.,8,True,https://github.com/usnistgov/chipsff/blob/main/LICENSE.rst,"['benchmarking', 'structure-optimization', 'md', 'materials-discovery', 'transport-phenomena']",usnistgov/chipsff,https://github.com/usnistgov/chipsff,2024-04-02 17:22:32,2025-05-02 20:59:43.000000,2025-02-06 21:18:44,213.0,,5.0,2.0,12.0,1.0,1.0,45.0,2024-12-11 23:18:42.000,2024.11.30,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +305,SIMPLE-NN v2,,ml-iap,https://github.com/MDIL-SNU/SIMPLE-NN_v2,SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab..,8,False,GPL-3.0,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-12-29 02:08:47.000000,2023-12-29 02:08:47,504.0,,18.0,5.0,88.0,2.0,11.0,43.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +306,Atomistic Adversarial Attacks,,ml-iap,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,MIT,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000000,2022-10-03 16:19:29,33.0,,8.0,4.0,1.0,,1.0,38.0,2021-07-19 18:09:36.000,1.0.1,1.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +307,SNAP,,ml-iap,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,BSD-3-Clause,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000000,2020-06-30 05:20:37,38.0,,17.0,10.0,1.0,1.0,3.0,36.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +308,ALF,,ml-iap,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,8,True,https://github.com/lanl/ALF/blob/main/LICENSE,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2025-04-09 17:44:07.000000,2025-03-28 13:28:33,153.0,,12.0,7.0,28.0,,,36.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +309,UVVisML,,rep-learn,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,False,MIT,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000000,2023-05-26 22:35:14,17.0,,8.0,3.0,1.0,,1.0,32.0,2022-02-06 18:14:14.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +310,GNNOpt,,rep-learn,https://github.com/nguyen-group/GNNOpt,Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures.,8,True,MIT,"['optical-properties', 'single-paper']",nguyen-group/GNNOpt,https://github.com/nguyen-group/GNNOpt,2024-06-26 12:39:27,2024-12-19 15:08:25.000000,2024-12-19 15:08:21,24.0,,8.0,3.0,,,,31.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +311,PACE,,md,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",8,True,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2025-01-08 14:34:48.000000,2024-12-17 09:53:39,63.0,,13.0,6.0,19.0,2.0,6.0,29.0,2025-01-08 14:34:49.000,.2023.11.25.fix2,9.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +312,polyVERSE,,datasets,https://github.com/Ramprasad-Group/polyVERSE,polyVERSE is a comprehensive repository of informatics-ready datasets curated by the Ramprasad Group.,8,True,https://github.com/Ramprasad-Group/polyVERSE?tab=License-1-ov-file,['soft-matter'],Ramprasad-Group/polyVERSE,https://github.com/Ramprasad-Group/polyVERSE,2024-03-26 02:42:47,2025-05-27 15:41:36.000000,2025-05-27 15:41:35,61.0,16.0,4.0,2.0,2.0,,,19.0,2024-08-21 04:09:16.000,1.0.0,1.0,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +313,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap,The TurboGAP code.,8,True,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2025-06-11 10:58:28.000000,2025-06-06 14:50:07,325.0,3.0,11.0,9.0,9.0,7.0,4.0,17.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +314,T-e3nn,,rep-learn,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,True,MIT,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2024-09-29 08:13:51.000000,2024-09-29 08:13:51,2146.0,,1.0,1.0,,,,15.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +315,bVAE-IM,,generative,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,False,MIT,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000000,2023-07-11 04:39:24,39.0,,4.0,8.0,,,,12.0,2023-03-01 14:26:13.000,1.0.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +316,CiderPress,,ml-dft,https://github.com/mir-group/CiderPress,A high-performance software package for training and evaluating machine-learned XC functionals using the CIDER..,8,True,GPL-3.0,"['ml-functional', 'lang-c']",mir-group/CiderPress,https://github.com/mir-group/CiderPress,2024-04-23 15:54:58,2025-06-24 16:11:10.000000,2025-04-09 19:35:11,380.0,1.0,2.0,2.0,21.0,,,12.0,2025-03-13 13:48:54.000,0.4.0,6.0,2.0,ciderpress,,,,https://pypi.org/project/ciderpress,2025-03-13 13:48:54.000,,41.0,41.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +317,optimade.science,,community,https://optimade.science,A sky-scanner Optimade browser-only GUI.,8,True,MIT,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2025-05-17 13:40:32.000000,2025-05-17 13:40:31,248.0,1.0,2.0,3.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25.000,2.0.0,1.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +318,MEGNetSparse,,ml-iap,https://github.com/HSE-LAMBDA/MEGNetSparse,"A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..",8,False,MIT,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2024-10-17 14:05:03.000000,2024-10-17 14:04:59,24.0,,1.0,1.0,,,,4.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,2.0,2.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,32.0,32.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +319,MADICES Awesome Interoperability,,community,MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md,Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..,8,False,MIT,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2025-05-20 06:09:16.000000,2025-03-16 12:33:56,225.0,,6.0,5.0,24.0,2.0,15.0,1.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +320,The Collection of Database and Dataset Resources in Materials Science,,community,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",7,True,,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2025-05-21 10:48:28.000000,2025-05-21 10:48:27,33.0,1.0,54.0,13.0,2.0,,3.0,345.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +321,GEOM,,datasets,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,7,False,,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000000,2022-04-24 18:57:39,95.0,,28.0,9.0,,2.0,11.0,228.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +322,tensorfieldnetworks,,rep-learn,https://github.com/tensorfieldnetworks/tensorfieldnetworks,Rotation- and translation-equivariant neural networks for 3D point clouds.,7,False,MIT,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000000,2020-01-07 17:22:15,10.0,,32.0,9.0,2.0,1.0,2.0,157.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +323,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,MIT,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000000,2022-02-18 13:37:51,8.0,,23.0,8.0,,,,132.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +324,PhysNet,,ml-iap,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,MIT,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000000,2020-12-07 11:09:20,4.0,,27.0,8.0,1.0,5.0,,105.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +325,DeepH-E3,,ml-dft,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,7,False,MIT,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000000,2023-04-04 13:26:27,16.0,,24.0,6.0,,27.0,10.0,97.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +326,Awesome-Crystal-GNNs,,community,https://github.com/kdmsit/Awesome-Crystal-GNNs,This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials.,7,True,MIT,,kdmsit/Awesome-Crystal-GNNs,https://github.com/kdmsit/Awesome-Crystal-GNNs,2022-11-15 11:12:18,2025-05-28 05:38:42.000000,2025-05-28 05:38:37,41.0,1.0,11.0,4.0,2.0,,,96.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +327,ElemNet,,rep-eng,https://github.com/NU-CUCIS/ElemNet,Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction.,7,False,,['single-paper'],NU-CUCIS/ElemNet,https://github.com/NU-CUCIS/ElemNet,2018-10-09 20:09:31,2023-03-25 01:33:04.000000,2022-06-22 05:58:46,79.0,,35.0,4.0,24.0,2.0,4.0,95.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +328,JAXChem,,general-tool,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,MIT,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000000,2020-07-15 04:55:41,96.0,,10.0,6.0,13.0,1.0,1.0,80.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +329,DTNN,,rep-learn,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,MIT,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000000,2017-07-11 08:25:39,9.0,,31.0,13.0,,,3.0,77.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +330,molecular-vae,,generative,https://github.com/aksub99/molecular-vae,Pytorch implementation of the paper Automatic Chemical Design Using a Data-Driven Continuous Representation of..,7,False,MIT,"['rep-learn', 'cheminformatics', 'single-paper']",aksub99/molecular-vae,https://github.com/aksub99/molecular-vae,2019-05-19 15:59:51,2021-03-31 13:16:36.000000,2021-03-31 13:16:36,68.0,,15.0,4.0,29.0,3.0,1.0,65.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +331,DeeperGATGNN,,rep-learn,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,7,False,MIT,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2024-01-19 18:11:52.000000,2024-01-19 18:11:38,25.0,,8.0,1.0,1.0,4.0,8.0,62.0,2022-03-08 02:14:28.000,1.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +332,cG-SchNet,,generative,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,7,False,MIT,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000000,2023-03-24 12:09:56,28.0,,14.0,3.0,,,3.0,61.0,2022-02-21 13:36:41.000,1.0,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +333,Cormorant,,rep-learn,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,https://github.com/risilab/cormorant/blob/master/LICENSE,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000000,2020-03-11 15:25:51,160.0,,10.0,5.0,1.0,3.0,,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +334,Graph-Aware-Transformers,,rep-learn,https://github.com/lamm-mit/Graph-Aware-Transformers,Graph-Aware Attention for Adaptive Dynamics in Transformers.,7,True,Apache-2.0,"['transformer', 'graph-data', 'pretrained', 'single-paper']",lamm-mit/Graph-Aware-Transformers,https://github.com/lamm-mit/Graph-Aware-Transformers,2025-01-03 12:29:24,2025-01-08 00:13:24.000000,2025-01-08 00:13:24,48.0,,7.0,2.0,3.0,1.0,,60.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +335,ChargE3Net,,ml-dft,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,7,True,MIT,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2025-02-21 18:35:27.000000,2025-02-21 18:35:27,17.0,,16.0,4.0,3.0,5.0,7.0,59.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +336,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,7,True,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2025-04-18 19:55:09.000000,2025-04-18 19:55:05,71.0,2.0,9.0,1.0,5.0,,11.0,57.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,9.0,deeperwin,,2.0,2.0,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,29.0,29.0,,,,3.0,,,,,,,15.0,,,,,,,,,,,,,, +337,Charting ML Publications in Science,,community,https://github.com/blaiszik/ml_publication_charts,"Literature analysis of ML applications in materials science, chemistry, physics.",7,True,MIT,"['literature-data', 'general-ml']",blaiszik/ml_publication_charts,https://github.com/blaiszik/ml_publication_charts,2019-06-09 00:07:07,2025-03-22 19:04:56.000000,2025-03-22 19:04:56,65.0,,,2.0,,3.0,,42.0,2025-03-22 19:01:40.000,2025.03,4.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +338,uncertainty_benchmarking,,general-tool,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000000,2021-06-07 23:27:19,265.0,,7.0,5.0,1.0,,,42.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +339,GAP,,ml-iap,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,7,True,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2025-04-22 20:49:37.000000,2025-04-22 20:49:37,208.0,1.0,20.0,9.0,70.0,,,42.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +340,chemlift,,language-models,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,7,False,MIT,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50.000000,2023-10-14 16:50:14,36.0,,7.0,1.0,1.0,11.0,7.0,42.0,2023-11-30 19:42:07.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +341,PolyGNN,,rep-learn,https://github.com/Ramprasad-Group/polygnn,polyGNN is a Python library to automate ML model training for polymer informatics.,7,True,MIT,"['soft-matter', 'multitask', 'single-paper']",Ramprasad-Group/polygnn,https://github.com/Ramprasad-Group/polygnn,2022-06-01 22:35:45,2025-02-05 19:55:48.000000,2025-02-05 19:54:37,91.0,,9.0,2.0,6.0,,20.0,41.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +342,AdsorbML,,rep-learn,https://github.com/Open-Catalyst-Project/AdsorbML,,7,True,MIT,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2025-02-05 17:20:34.000000,2025-02-05 17:20:34,59.0,,6.0,8.0,11.0,3.0,1.0,40.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +343,LLM-Prop,,language-models,https://github.com/vertaix/LLM-Prop,A repository for the LLM-Prop implementation.,7,False,MIT,,vertaix/LLM-Prop,https://github.com/vertaix/LLM-Prop,2022-10-16 19:15:21,2024-04-26 14:20:54.000000,2024-04-26 14:20:54,175.0,,7.0,2.0,,1.0,1.0,39.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +344,torchchem,,general-tool,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,MIT,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000000,2020-05-01 20:12:23,49.0,,14.0,7.0,27.0,5.0,1.0,36.0,,,,5.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +345,scdp (scalable charge density prediction),,ml-dft,https://github.com/kyonofx/scdp,[NeurIPS 2024] source code for A Recipe for Charge Density Prediction.,7,True,MIT,"['rep-learn', 'single-paper']",kyonofx/scdp,https://github.com/kyonofx/scdp,2024-09-28 01:15:35,2024-12-17 07:44:44.000000,2024-12-17 07:44:44,12.0,,12.0,3.0,,1.0,4.0,35.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +346,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,False,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000000,2023-06-28 14:39:56,203.0,,2.0,,,,,30.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +347,CGAT,,rep-learn,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,7,False,MIT,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000000,2023-01-10 22:31:07,153.0,,9.0,2.0,1.0,,1.0,28.0,2023-07-18 12:04:35.000,0.1,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +348,Mat2Spec,,ml-dft,https://github.com/gomes-lab/Mat2Spec,Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.,7,False,MIT,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29.000000,2022-04-17 17:12:29,8.0,,11.0,,,,,28.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +349,Libnxc,,ml-dft,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,MPL-2.0,"['lang-cpp', 'lang-fortran']",semodi/libnxc,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,20.0,,,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +350,NICE,,rep-eng,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,7,False,MIT,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2024-04-15 14:39:34.000000,2024-04-15 14:39:33,233.0,,3.0,15.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +351,SISSO++,,rep-eng,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,7,False,Apache-2.0,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000000,,,,4.0,,,2.0,23.0,3.0,,,3.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,, +352,BPNET,,ml-iap,https://github.com/cometscome/BPNET,Fast Behler-Parrinello type neural networks in Fortran2008.,7,False,MIT,"['rep-eng', 'lang-fortran']",cometscome/BPNET,https://github.com/cometscome/BPNET,2025-02-07 01:05:45,2025-05-12 07:31:55.000000,2025-05-12 07:31:55,18.0,4.0,,1.0,,,,3.0,2025-02-07 06:54:49.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +353,ACEpsi.jl,,ml-wft,https://github.com/ACEsuit/ACEpsi.jl,ACE wave function parameterizations.,7,False,MIT,"['rep-eng', 'lang-julia']",ACEsuit/ACEpsi.jl,https://github.com/ACEsuit/ACEpsi.jl,2022-10-21 03:51:18,2025-07-03 06:51:30.000000,2023-10-05 21:21:35,162.0,,,4.0,17.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +354,MAChINE,,educational,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,7,False,MIT,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000000,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +355,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,7,False,Apache-2.0,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000000,2023-01-10 19:49:13,1336.0,,,,,,,1.0,,,,17.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +356,Geom3D,,rep-learn,https://github.com/chao1224/Geom3D,"Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023.",6,False,MIT,"['benchmarking', 'single-paper']",chao1224/Geom3D,https://github.com/chao1224/Geom3D,2023-06-07 17:27:56,2024-06-05 03:18:58.000000,2023-08-11 21:33:20,9.0,,13.0,2.0,4.0,4.0,,121.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +357,COATI,,generative,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,6,False,Apache 2.0,"['drug-discovery', 'multimodal', 'pretrained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2024-03-23 18:06:26.000000,2024-03-23 18:06:26,16.0,,7.0,3.0,7.0,1.0,2.0,112.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +358,crystal-text-llm,,language-models,https://github.com/facebookresearch/crystal-text-llm,Large language models to generate stable crystals.,6,False,CC-BY-NC-4.0,['materials-discovery'],facebookresearch/crystal-text-llm,https://github.com/facebookresearch/crystal-text-llm,2024-02-05 22:29:12,2024-06-18 17:10:52.000000,2024-06-18 17:10:52,13.0,,22.0,3.0,3.0,12.0,2.0,108.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +359,LLM4Chem,,language-models,https://github.com/OSU-NLP-Group/LLM4Chem,"Official code repo for the paper LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,..",6,True,MIT,"['cheminformatics', 'datasets']",OSU-NLP-Group/LLM4Chem,https://github.com/OSU-NLP-Group/LLM4Chem,2024-02-13 22:29:28,2025-06-09 05:13:02.000000,2025-06-09 05:13:02,11.0,2.0,13.0,7.0,1.0,,9.0,90.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +360,MLforMaterials,,educational,https://github.com/aronwalsh/MLforMaterials,Online resource for a practical course in machine learning for materials research at Imperial College London..,6,True,MIT,"['community', 'general-ml', 'rep-eng', 'materials-discovery']",aronwalsh/MLforMaterials,https://github.com/aronwalsh/MLforMaterials,2023-08-02 14:29:12,2025-06-01 08:50:06.000000,2025-02-17 16:45:52,72.0,,13.0,2.0,2.0,,2.0,85.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +361,DeepDFT,,ml-dft,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,False,MIT,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000000,2023-02-28 15:37:37,128.0,,10.0,1.0,,,5.0,79.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +362,SchNOrb,,ml-wft,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,6,False,MIT,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000000,2019-09-17 14:31:19,2.0,,21.0,4.0,,1.0,,65.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +363,ANI-1x Datasets,,datasets,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,MIT,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000000,2022-04-11 17:25:55,12.0,,5.0,4.0,,4.0,3.0,64.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +364,Applied AI for Materials,,educational,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000000,2022-03-12 02:26:41,107.0,,38.0,3.0,13.0,5.0,,64.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +365,EScAIP,,uip,https://github.com/ASK-Berkeley/EScAIP,[NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential.,6,True,MIT,"['ml-iap', 'rep-learn', 'transformer', 'single-paper']",ASK-Berkeley/EScAIP,https://github.com/ASK-Berkeley/EScAIP,2024-08-09 20:02:18,2025-03-11 20:22:44.000000,2025-03-06 17:11:33,31.0,,5.0,5.0,9.0,4.0,2.0,51.0,,,2.0,2.0,,,,,,,,,0.0,,,,3.0,,,,,,,5.0,,,,,,,,,,,,,, +366,MACE-tutorials,,educational,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,6,True,MIT,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-09-14 17:54:11.000000,2024-07-16 12:45:42,7.0,,12.0,3.0,,1.0,,47.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +367,COMP6 Benchmark dataset,,datasets,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,MIT,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000000,2018-07-09 23:56:34,27.0,,4.0,4.0,,2.0,1.0,40.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +368,MACE-Layer,,rep-learn,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,False,MIT,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000000,2023-06-06 10:09:58,19.0,,12.0,4.0,2.0,1.0,,40.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +369,MOLPIPx,,rep-eng,https://github.com/ChemAI-Lab/molpipx,Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust.,6,True,Apache-2.0,"['lang-py', 'lang-rust']",ChemAI-Lab/molpipx,https://github.com/ChemAI-Lab/molpipx,2023-04-12 21:17:33,2025-04-14 21:56:53.000000,2025-04-14 21:56:53,152.0,2.0,1.0,1.0,3.0,,,37.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +370,charge_transfer_nnp,,rep-learn,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,MIT,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000000,2022-04-06 01:53:22,1.0,,8.0,10.0,,1.0,,35.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +371,milad,,rep-eng,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,GPL-3.0,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2024-08-20 12:50:12.000000,2024-08-20 12:50:10,111.0,,2.0,3.0,,,,32.0,,,,1.0,,,3.0,3.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +372,MLIP-3,,ml-iap,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,6,False,BSD-2-Clause,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000000,,,,9.0,,,26.0,7.0,24.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,, +373,GLAMOUR,,rep-learn,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,False,MIT,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000000,2022-12-31 17:56:21,14.0,,7.0,3.0,1.0,1.0,8.0,23.0,2021-08-23 18:58:52.000,0.1,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +374,ffonons,,uip,https://github.com/janosh/ffonons,Phonons from ML force fields.,6,True,MIT,"['benchmarking', 'density-of-states']",janosh/ffonons,https://github.com/janosh/ffonons,2023-11-16 02:34:22,2025-04-07 16:37:49.000000,2024-12-08 21:54:38,62.0,,2.0,2.0,7.0,1.0,,21.0,2024-01-10 11:39:59.000,0.1.0,1.0,2.0,ffonons,,2.0,2.0,https://pypi.org/project/ffonons,2024-01-10 11:39:59.000,,18.0,18.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +375,EquivariantOperators.jl,,math,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,False,MIT,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000000,2023-09-27 18:34:44,62.0,,,4.0,,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +376,DeepModeling Tutorials,,educational,https://github.com/deepmodeling/tutorials,Tutorials for DeepModeling projects.,6,True,,,deepmodeling/tutorials,https://github.com/deepmodeling/tutorials,2022-03-07 06:19:19,2025-04-03 06:16:39.000000,2025-04-03 06:16:39,146.0,,23.0,5.0,67.0,,3.0,15.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +377,BERT-PSIE-TC,,language-models,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,6,False,MIT,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000000,2023-08-18 12:48:31,36.0,,3.0,,,,,15.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +378,SciGlass,,datasets,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,6,False,MIT,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000000,2023-08-27 13:46:44,28.0,,3.0,,,,,14.0,2023-08-27 13:48:09.000,2.0.1,1.0,2.0,,,,,,,,,1.0,,,,3.0,,,,,,,41.0,,,,,,,,,,,,,, +379,rxngenerator,,generative,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,6,False,MIT,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21.000000,2022-08-09 07:21:05,16.0,,3.0,8.0,2.0,1.0,,14.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +380,charge-density-models,,ml-dft,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem).,6,False,MIT,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000000,2023-11-29 15:07:42,96.0,,3.0,1.0,16.0,1.0,3.0,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +381,CatGym,,reinforcement-learning,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,GPL,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000000,2021-08-30 17:05:32,162.0,,2.0,3.0,,2.0,,12.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +382,testing-framework,,ml-iap,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000000,2022-02-10 17:23:46,225.0,,8.0,15.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +383,PANNA,,ml-iap,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,MIT,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000000,,,,10.0,,,,,11.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,, +384,Asparagus,,ml-iap,https://github.com/MMunibas/Asparagus,"Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175.",6,True,MIT,"['workflows', 'sampling', 'md']",MMunibas/Asparagus,https://github.com/MMunibas/Asparagus,2024-07-08 13:44:56,2025-04-09 13:24:58.000000,2025-04-09 13:24:52,115.0,1.0,5.0,1.0,5.0,,,11.0,,,3.0,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +385,ML for catalysis tutorials,,educational,https://github.com/ulissigroup/ml_catalysis_tutorials,A jupyter book repo for tutorial on how to use OCP ML models for catalysis.,6,False,MIT,,ulissigroup/ml_catalysis_tutorials,https://github.com/ulissigroup/ml_catalysis_tutorials,2022-10-28 20:37:30,2022-10-31 18:06:07.000000,2022-10-31 17:49:25,40.0,,1.0,3.0,,,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +386,Crystalframer,,rep-learn,https://github.com/omron-sinicx/crystalframer,The official code respository for Rethinking the role of frames for SE(3)-invariant crystal structure modeling (ICLR..,6,True,MIT,"['transformer', 'single-paper']",omron-sinicx/crystalframer,https://github.com/omron-sinicx/crystalframer,2025-01-30 06:14:55,2025-05-03 09:49:37.000000,2025-05-03 09:43:25,5.0,2.0,1.0,5.0,,,,9.0,2025-05-03 09:49:37.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +387,MLXDM,,ml-iap,https://github.com/RowleyGroup/MLXDM,A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K.,6,True,MIT,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2025-03-12 18:47:57.000000,2025-03-12 18:47:53,55.0,,2.0,4.0,,,,8.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +388,COSMO Toolbox,,math,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,False,,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2024-03-19 13:27:28.000000,2024-03-19 13:27:02,107.0,,7.0,26.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +389,fplib,,rep-eng,https://github.com/Rutgers-ZRG/libfp,libfp is a library for calculating crystalline fingerprints and measuring similarities of materials.,6,True,MIT,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/Rutgers-ZRG/libfp,2015-09-07 08:18:27,2025-04-16 16:28:43.000000,2025-04-16 16:28:37,63.0,5.0,1.0,2.0,1.0,,3.0,7.0,2024-09-26 20:12:39.000,3.1.2,3.0,2.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,Rutgers-ZRG/libfp,,,,,,,,,,,,, +390,SOAPxx,,rep-eng,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,GPL-2.0,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +391,soap_turbo,,rep-eng,https://github.com/libAtoms/soap_turbo,soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.,6,False,https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2025-06-06 14:49:11.000000,2023-05-24 09:42:00,36.0,,8.0,7.0,1.0,5.0,3.0,7.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +392,Equisolve,,general-tool,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,6,False,BSD-3-Clause,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000000,2023-10-27 09:55:17,55.0,,1.0,15.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +393,COSMO tools,,others,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",6,True,,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2025-06-21 09:21:43.000000,2025-06-21 09:21:19,67.0,2.0,5.0,22.0,,,,5.0,,,,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,True, +394,cnine,,math,https://github.com/risi-kondor/cnine,Cnine tensor library.,6,True,,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2025-06-14 04:47:34.000000,2025-06-14 04:47:33,544.0,37.0,4.0,2.0,11.0,1.0,1.0,5.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/ +395,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",6,False,GPL-3.0,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000000,2023-06-26 12:48:15,157.0,,14.0,2.0,1.0,,,4.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +396,KSR-DFT,,ml-dft,https://github.com/pedersor/ksr_dft,Kohn-Sham regularizer for machine-learned DFT functionals.,6,False,Apache-2.0,,pedersor/ksr_dft,https://github.com/pedersor/ksr_dft,2023-03-01 17:24:48,2023-03-04 07:20:22.000000,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +397,pyLODE,,rep-eng,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,Apache-2.0,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000000,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +398,ML4pXRDs,,rep-learn,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,6,False,MIT,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000000,2023-07-14 08:17:04,1320.0,,1.0,2.0,,,,3.0,2023-03-22 11:04:31.000,1.0,1.0,,,,,,,,,,0.0,,,,3.0,,,,,,,6.0,,,,,,,,,,,,,, +399,AI4Science101,,educational,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2024-04-11 02:15:55.000000,2022-09-04 02:06:18,139.0,,15.0,9.0,29.0,2.0,1.0,95.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +400,The Perovskite Database Project,,datasets,https://github.com/Jesperkemist/perovskitedatabase,"Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form..",5,False,,['community'],Jesperkemist/perovskitedatabase,https://github.com/Jesperkemist/perovskitedatabase,2021-01-17 14:26:45,2024-03-07 11:09:21.000000,2024-03-07 11:09:17,44.0,,22.0,2.0,7.0,1.0,,65.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +401,LapNet,,ml-wft,https://github.com/bytedance/LapNet,Efficient and Accurate Neural-Network Ansatz for Quantum Monte Carlo.,5,True,Apache-2.0,,bytedance/LapNet,https://github.com/bytedance/LapNet,2023-11-13 08:19:53,2024-12-04 05:37:32.000000,2024-12-04 05:37:32,9.0,,12.0,2.0,4.0,,1.0,64.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +402,Joint Multidomain Pre-Training (JMP),,uip,https://github.com/facebookresearch/JMP,Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction.,5,True,CC-BY-NC-4.0,"['pretrained', 'ml-iap', 'general-tool']",facebookresearch/JMP,https://github.com/facebookresearch/JMP,2024-03-14 23:10:10,2024-10-22 22:29:40.000000,2024-10-22 22:29:36,3.0,,7.0,3.0,1.0,2.0,3.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +403,Machine Learning for Materials Hard and Soft,,educational,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000000,2022-07-22 08:10:24,49.0,,20.0,1.0,14.0,,,39.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +404,xDeepH,,ml-dft,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,5,False,LGPL-3.0,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000000,2023-06-14 11:44:46,4.0,,4.0,3.0,,3.0,,37.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +405,SciBot,,language-models,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,5,True,,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-09-03 15:21:15.000000,2024-09-03 15:20:54,23.0,,9.0,6.0,,,,31.0,,,,1.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +406,Autobahn,,rep-learn,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,MIT,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000000,2022-03-01 21:04:04,11.0,,2.0,4.0,,,,29.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +407,ML-DFT,,ml-dft,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,5,False,MIT,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000000,2020-09-18 16:36:30,9.0,,10.0,1.0,,1.0,1.0,26.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +408,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,MIT,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-12-22 20:28:39.000000,2024-12-22 20:28:39,27.0,,8.0,1.0,,2.0,1.0,24.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +409,NequIP-JAX,,ml-iap,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,False,,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000000,2023-11-01 20:35:44,39.0,,3.0,2.0,2.0,2.0,2.0,23.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +410,SA-GPR,,rep-eng,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,5,True,LGPL-3.0,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2025-02-03 12:26:26.000000,2025-02-03 12:26:26,28.0,,17.0,3.0,11.0,3.0,5.0,20.0,2020-12-17 16:51:47.000,2020.0,1.0,6.0,,,,,,,,,0.0,,,,3.0,,,,,,,2.0,,,,,,,,,,,,,, +411,FieldSchNet,,rep-learn,https://github.com/atomistic-machine-learning/field_schnet,Deep neural network for molecules in external fields.,5,False,MIT,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000000,2022-05-19 09:28:38,26.0,,6.0,1.0,1.0,1.0,,19.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +412,DSM-CORE,,educational,https://matsciedu.github.io/DSM-CORE,Data Science for Materials - Collection of Open Educational Resources.,5,True,,,MatSciEdu/DSM-CORE,https://github.com/MatSciEdu/DSM-CORE,2024-10-03 16:29:54,2025-06-18 17:17:31.000000,2025-06-18 17:17:31,220.0,3.0,7.0,1.0,9.0,1.0,1.0,16.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +413,SOMD,,md,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,5,True,AGPL-3.0,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2025-06-11 21:32:28.000000,2025-06-11 21:32:10,340.0,7.0,2.0,1.0,12.0,,1.0,16.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +414,thermo,,probabilistic,https://github.com/janosh/thermo,Data-driven risk-conscious thermoelectric materials discovery.,5,True,MIT,"['materials-discovery', 'experimental-data', 'active-learning', 'transport-phenomena']",janosh/thermo,https://github.com/janosh/thermo,2019-07-30 16:27:10,2025-05-12 23:41:15.000000,2025-05-12 23:40:23,82.0,1.0,4.0,2.0,21.0,,4.0,16.0,,,,2.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +415,SCFNN,,rep-learn,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,MIT,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +416,CraTENet,,rep-learn,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,False,MIT,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000000,2023-04-05 01:13:11,24.0,,1.0,1.0,,2.0,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +417,InfGCN for Electron Density Estimation,,ml-dft,https://github.com/ccr-cheng/InfGCN-pytorch,Official implementation of the NeurIPS 23 spotlight paper of InfGCN.,5,False,MIT,"['rep-learn', 'neural-operator']",ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19.000000,2023-12-05 01:31:14,3.0,,4.0,1.0,,,3.0,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +418,ACEHAL,,active-learning,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,False,,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000000,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,12.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +419,QMLearn,,ml-esm,http://qmlearn.rutgers.edu/,Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...,5,False,MIT,,,,2022-02-15 13:42:13,2022-02-15 13:42:13.000000,,,,4.0,,,,,12.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,, +420,EGraFFBench,,rep-learn,https://github.com/M3RG-IITD/MDBENCHGNN,,5,False,,"['single-paper', 'benchmarking', 'ml-iap']",M3RG-IITD/MDBENCHGNN,https://github.com/M3RG-IITD/MDBENCHGNN,2023-07-06 18:15:34,2023-11-19 05:16:12.000000,2023-11-19 05:14:44,161.0,,,,,4.0,,11.0,2023-07-16 05:46:38.000,0.1.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +421,GN-MM,,ml-iap,https://gitlab.com/zaverkin_v/gmnn,The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic..,5,False,MIT,"['active-learning', 'md', 'rep-eng', 'magnetism']",,,2021-09-19 15:56:31,2021-09-19 15:56:31.000000,,,,4.0,,,,,11.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,, +422,TensorPotential,,ml-iap,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",5,True,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2024-09-12 10:19:56.000000,2024-09-12 10:19:56,22.0,,5.0,1.0,2.0,,,10.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +423,Cephalo,,language-models,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,5,True,Apache-2.0,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58.000000,2024-07-23 09:27:57,24.0,,1.0,1.0,,,,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +424,MAPI_LLM,,language-models,https://github.com/maykcaldas/MAPI_LLM,A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J.,5,False,MIT,"['ai-agent', 'dataset']",maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2024-04-20 03:16:17.000000,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,9.0,2023-06-29 18:48:44.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +425,GDB-9-Ex9 and ORNL_AISD-Ex,,datasets,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-..,5,True,,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2025-03-12 21:52:10.000000,2025-03-12 21:52:10,59.0,,6.0,5.0,16.0,2.0,,8.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +426,MatML,,ml-iap,https://github.com/materialsvirtuallab/matml,"Full MatML Docker image, including MatGL, MatCalc, MatPES and LAMMPS with ML-GNNP and ML-SNAP.",5,True,BSD-3-Clause,"['md', 'uip', 'rep-learn', 'pretrained']",materialsvirtuallab/matml,https://github.com/materialsvirtuallab/matml,2025-04-01 20:08:28,2025-06-02 20:09:05.000000,2025-06-02 20:09:05,53.0,17.0,,1.0,2.0,,1.0,7.0,,,,2.0,,,,,,,,,41.0,,,,3.0,,materialsvirtuallab/matml,https://hub.docker.com/r/materialsvirtuallab/matml,2025-04-08 02:43:39.769975,1.0,123.0,,,,,,,,,,,,,,, +427,MolSLEPA,,generative,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,False,MIT,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +428,MXenes4HER,,rep-eng,https://github.com/cnislab/MXenes4HER,Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.,5,False,GPL-3.0,"['materials-discovery', 'catalysis', 'scikit-learn', 'single-paper']",cnislab/MXenes4HER,https://github.com/cnislab/MXenes4HER,2022-11-28 09:27:36,2023-02-27 18:08:05.000000,2023-02-27 18:08:05,67.0,,4.0,1.0,1.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +429,MUSE,,md,https://github.com/chiang-yuan/muse,A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and..,5,True,MIT,"['ml-iap', 'defects-disorder']",chiang-yuan/muse,https://github.com/chiang-yuan/muse,2023-07-29 00:36:06,2025-05-15 22:01:45.000000,2025-05-15 22:01:41,59.0,2.0,,1.0,9.0,2.0,,6.0,2024-09-18 01:32:57.000,0.1.1,1.0,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +430,q-pac,,ml-esm,https://gitlab.com/jmargraf/qpac,Kernel charge equilibration method.,5,False,MIT,['electrostatics'],,,2020-11-15 20:11:27,2020-11-15 20:11:27.000000,,,,4.0,,,2.0,,5.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,, +431,paper-ml-robustness-material-property,,unsupervised,https://github.com/mathsphy/paper-ml-robustness-material-property,A critical examination of robustness and generalizability of machine learning prediction of materials properties.,5,False,BSD-3-Clause,"['datasets', 'single-paper']",mathsphy/paper-ml-robustness-material-property,https://github.com/mathsphy/paper-ml-robustness-material-property,2023-02-21 02:38:13,2023-04-13 01:18:02.000000,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +432,rho_learn,,ml-dft,https://github.com/m-stack-org/rho_learn,A proof-of-concept workflow for torch-based electron density learning.,5,False,MIT,"['ml-dft', 'rep-eng']",m-stack-org/rho_learn,https://github.com/m-stack-org/rho_learn,2023-02-14 15:46:26,2023-04-03 07:03:02.000000,2023-03-27 16:58:46,98.0,,2.0,1.0,1.0,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +433,halex,,ml-esm,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,5,False,,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53.000000,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,3.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +434,rholearn,,ml-dft,https://github.com/lab-cosmo/rholearn,Learning and predicting electronic densities decomposed on a basis and global electronic densities of states at DFT..,5,False,MIT,"['ml-dft', 'rep-eng', 'density-of-states']",lab-cosmo/rholearn,https://github.com/lab-cosmo/rholearn,2024-09-30 20:22:25,2025-05-06 15:57:41.000000,2025-01-24 13:12:39,18.0,,1.0,2.0,13.0,,,3.0,2024-10-04 19:31:33.000,0.1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +435,Alchemical learning,,ml-iap,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,BSD-3-Clause,"['rep-eng', 'defects-disorder']",Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000000,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +436,Per-site PAiNN,,rep-learn,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,False,MIT,"['probabilistic', 'pretrained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000000,2023-06-05 17:30:34,123.0,,1.0,,,,,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +437,ACE1Pack.jl,,ml-iap,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",5,False,MIT,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000000,2023-08-21 15:48:54,547.0,,,1.0,,,,1.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl +438,Per-Site CGCNN,,rep-learn,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,MIT,"['pretrained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +439,AMP,,rep-eng,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,5,False,,,,,,2023-01-25 17:30:41.112000,,,,25.0,,,,,,2023-01-25 17:30:41.112,1.0.1,3.0,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,2023-01-25 17:30:41.112,,20.0,20.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/ +440,Geometric-GNNs,,community,https://github.com/AlexDuvalinho/geometric-gnns,List of Geometric GNNs for 3D atomic systems.,4,False,,"['datasets', 'educational', 'rep-learn']",AlexDuvalinho/geometric-gnns,https://github.com/AlexDuvalinho/geometric-gnns,2023-08-31 09:10:32,2024-02-29 16:25:54.000000,2024-02-29 16:25:53,37.0,,6.0,1.0,3.0,,1.0,113.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +441,ML-in-chemistry-101,,educational,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000000,2020-10-19 08:10:30,13.0,,19.0,1.0,,,,78.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +442,MAGUS,,materials-discovery,https://gitlab.com/bigd4/magus,Machine learning And Graph theory assisted Universal structure Searcher.,4,False,,"['structure-prediction', 'active-learning']",,,2023-01-31 09:00:23,2023-01-31 09:00:23.000000,,,,19.0,,,,,76.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,, +443,3D-EMGP,,unsupervised,https://github.com/jiaor17/3D-EMGP,[AAAI 2023] The implementation for the paper Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs.,4,False,MIT,"['pretrained', 'rep-learn', 'single-paper']",jiaor17/3D-EMGP,https://github.com/jiaor17/3D-EMGP,2022-05-26 08:10:41,2024-09-14 00:37:28.000000,2024-09-14 00:19:53,20.0,,7.0,2.0,,,1.0,34.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +444,CatBERTa,,language-models,https://github.com/hoon-ock/CatBERTa,Large Language Model for Catalyst Property Prediction.,4,False,,"['transformer', 'catalysis']",hoon-ock/CatBERTa,https://github.com/hoon-ock/CatBERTa,2023-05-19 18:23:17,2024-03-08 02:59:22.000000,2024-03-08 02:59:22,93.0,,4.0,1.0,2.0,,,26.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +445,glp,,ml-iap,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,MIT,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2024-04-09 12:06:56.000000,2024-03-20 09:00:27,11.0,,1.0,1.0,3.0,,,25.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +446,Coarse-Graining-Auto-encoders,,unsupervised,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,Implementation of coarse-graining Autoencoders.,4,False,,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000000,2019-08-16 21:39:33,14.0,,7.0,5.0,,,,21.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +447,ALEBREW,,active-learning,https://github.com/nec-research/alebrew,Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic..,4,False,https://github.com/nec-research/alebrew/blob/main/LICENSE.txt,"['ml-iap', 'md']",nec-research/alebrew,https://github.com/nec-research/alebrew,2024-02-27 07:32:23,2024-10-29 15:11:38.000000,2024-10-29 15:11:33,3.0,,6.0,1.0,,,1.0,21.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +448,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,4,False,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2024-11-21 18:12:10.000000,2024-11-21 18:11:56,64.0,,5.0,2.0,,1.0,1.0,20.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +449,Allegro-Legato,,ml-iap,https://github.com/ibayashi-hikaru/allegro-legato,An extension of Allegro with enhanced robustness and time-to-failure.,4,False,MIT,['md'],ibayashi-hikaru/allegro-legato,https://github.com/ibayashi-hikaru/allegro-legato,2023-01-17 19:46:10,2023-08-03 22:25:11.000000,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,20.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +450,Crystalformer,,rep-learn,https://github.com/omron-sinicx/crystalformer,The official code respository for Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding (ICLR..,4,False,MIT,"['transformer', 'single-paper']",omron-sinicx/crystalformer,https://github.com/omron-sinicx/crystalformer,2024-03-15 00:33:16,2025-03-08 08:05:09.000000,2025-03-08 08:05:09,7.0,,1.0,4.0,,1.0,2.0,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +451,Does this material exist?,,community,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,4,False,MIT,"['for-fun', 'materials-discovery']",ml-evs/this-material-does-not-exist,https://github.com/ml-evs/this-material-does-not-exist,2023-12-01 18:16:28,2024-07-29 09:50:18.000000,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,18.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +452,LAM Crystal Philately competition 2024,,community,https://bohrium.dp.tech/competitions/8821838186,OpenLAM Challenge crystal structure prediction https://arxiv.org/abs/2501.16358.,4,False,LGPL-2.1,"['single-paper', 'datasets', 'structure-prediction', 'materials-discovery', 'ml-iap', 'uip']",deepmodeling/openlam,https://github.com/deepmodeling/openlam,2024-04-22 06:31:11,2025-02-10 03:36:17.000000,2025-02-10 03:36:17,33.0,,3.0,9.0,11.0,,1.0,18.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +453,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000000,2023-04-26 14:22:00,9.0,,3.0,1.0,,,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +454,SPINNER,,materials-discovery,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,4,False,GPL-3.0,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50.000000,2021-11-25 07:58:15,102.0,,3.0,1.0,,1.0,,14.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +455,paper-data-redundancy,,datasets,https://github.com/mathsphy/paper-data-redundancy,Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.,4,False,BSD-3-Clause,"['small-data', 'single-paper']",mathsphy/paper-data-redundancy,https://github.com/mathsphy/paper-data-redundancy,2023-06-10 15:00:28,2024-09-23 13:37:50.000000,2024-09-23 13:37:49,18.0,,1.0,1.0,,,,11.0,2023-10-11 14:09:07.000,1.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +456,chemrev-gpr,,educational,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000000,2021-05-04 19:21:30,10.0,,8.0,4.0,,,,11.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +457,Mapping out phase diagrams with generative classifiers,,generative,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,Repository for our ``Mapping out phase diagrams with generative models paper.,4,False,MIT,['phase-transition'],arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,2023-06-07 21:43:14,2023-06-27 08:12:29.000000,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,8.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +458,gkx: Green-Kubo Method in JAX,,rep-learn,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,4,False,MIT,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2025-02-19 15:56:12.000000,2024-03-20 09:05:14,3.0,,1.0,1.0,1.0,,,7.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +459,KmdPlus,,unsupervised,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",4,False,MIT,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2024-09-25 07:36:49.000000,2024-09-25 07:36:48,8.0,,1.0,1.0,,,,7.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +460,DeepCDP,,ml-dft,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,4,False,,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000000,2023-06-16 20:38:23,96.0,,3.0,1.0,27.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +461,ML-atomate,,materials-discovery,https://github.com/takahashi-akira-36m/ml_atomate,Machine learning-assisted Atomate code for autonomous computational materials screening.,4,False,GPL-3.0,"['active-learning', 'workflows']",takahashi-akira-36m/ml_atomate,https://github.com/takahashi-akira-36m/ml_atomate,2023-09-21 08:45:10,2023-11-17 09:54:23.000000,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,6.0,2023-09-29 03:52:46.000,stam_m_2023_fix,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +462,descriptors-inversion,,generative,https://github.com/MCobe94/descriptors-inversion,Local inversion of the chemical environment representations.,4,False,MIT,"['rep-eng', 'single-paper']",MCobe94/descriptors-inversion,https://github.com/MCobe94/descriptors-inversion,2022-01-27 14:55:20,2023-07-14 14:46:29.000000,2023-07-14 14:46:29,19.0,,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +463,automl-materials,,rep-eng,https://github.com/mm-tud/automl-materials,AutoML for Regression Tasks on Small Tabular Data in Materials Design.,4,False,MIT,"['automl', 'benchmarking', 'single-paper']",mm-tud/automl-materials,https://github.com/mm-tud/automl-materials,2022-10-07 09:49:18,2022-11-15 15:22:54.000000,2022-11-15 15:22:45,6.0,,1.0,1.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +464,OPTIMADE providers dashboard,,datasets,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,4,False,,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2025-07-03 06:52:33.000000,2025-05-04 11:00:25,143.0,1.0,3.0,18.0,149.0,11.0,18.0,2.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +465,linear-regression-benchmarks,,datasets,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,4,False,MIT,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000000,2022-01-26 08:29:46,24.0,,,2.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +466,AI4ChemMat Hands-On Series,,educational,https://github.com/ai4chemmat/ai4chemmat.github.io,Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.,4,False,MPL-2.0,,ai4chemmat/ai4chemmat.github.io,https://github.com/ai4chemmat/ai4chemmat.github.io,2023-03-24 21:25:21,2024-04-24 16:32:18.000000,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +467,ACE Workflows,,ml-iap,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,4,False,,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000000,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +468,gprep,,ml-dft,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,MIT,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,, +469,closed-loop-acceleration-benchmarks,,materials-discovery,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,MIT,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +470,PeriodicPotentials,,ml-iap,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,MIT,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +471,Allegro-JAX,,ml-iap,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,3,False,MIT,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2025-05-07 10:07:14.000000,2025-05-07 10:07:10,8.0,1.0,2.0,2.0,1.0,1.0,2.0,22.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +472,ChemDataWriter,,language-models,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,3,False,MIT,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000000,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +473,MEGAN: Multi Explanation Graph Attention Student,,xai,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,3,False,MIT,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2025-05-19 09:19:08.000000,2025-05-19 09:19:04,106.0,1.0,2.0,2.0,1.0,1.0,2.0,11.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +474,atom_by_atom,,rep-learn,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000000,2023-10-19 15:35:49,74.0,,1.0,2.0,,,,10.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +475,e3psi,,ml-esm,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,LGPL-3.0,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2024-01-05 12:59:56.000000,2024-01-05 12:59:09,19.0,,,1.0,,,,7.0,,,,,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +476,Element encoder,,rep-learn,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,GPL-3.0,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000000,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +477,sl_discovery,,materials-discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,Apache-2.0,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000000,2022-12-20 23:45:57,5.0,,1.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +478,APET,,ml-dft,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,3,False,GPL-3.0,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2024-04-24 03:43:39.000000,2023-09-28 03:16:11,11.0,,,1.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +479,Visual Graph Datasets,,datasets,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,3,False,MIT,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2025-04-24 12:08:13.000000,2025-04-24 12:08:03,57.0,1.0,2.0,2.0,,1.0,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +480,interface-lammps-mlip-3,,md,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,3,False,GPL-2.0,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000000,,,,7.0,,,4.0,1.0,4.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,, +481,PiNN Lab,,educational,https://github.com/Teoroo-CMC/PiNN_lab,Material for running a lab session on atomic neural networks.,3,False,GPL-3.0,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000000,2023-05-01 15:59:22,9.0,,1.0,2.0,1.0,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +482,quantum-structure-ml,,general-tool,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,3,False,,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000000,2022-12-22 21:45:40,19.0,,,2.0,,,,3.0,2022-08-18 05:25:24.000,1.0.0,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +483,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,3,False,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000000,2023-01-13 21:28:08,134.0,,1.0,2.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +484,CSNN,,ml-dft,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,BSD-3-Clause,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000000,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +485,Linear vs blackbox,,xai,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,MIT,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +486,ML-for-CurieTemp-Predictions,,rep-eng,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,3,False,MIT,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000000,2023-06-14 19:05:47,25.0,,,1.0,,,,2.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +487,torch_spex,,math,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,3,False,,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2023-12-13 16:39:24.000000,,,,2.0,,,10.0,,2.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +488,MALADA,,ml-dft,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,BSD-3-Clause,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2025-04-30 10:35:41.000000,2025-04-30 10:35:37,135.0,4.0,2.0,2.0,6.0,17.0,2.0,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +489,magnetism-prediction,,rep-eng,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,3,False,Apache-2.0,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2025-04-20 15:51:23.000000,2025-04-20 15:51:23,48.0,2.0,1.0,2.0,,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +490,Magpie,,general-tool,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,MIT,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +491,PyFLAME,,ml-iap,https://gitlab.com/flame-code/PyFLAME,An automated approach for developing neural network interatomic potentials with FLAME..,3,False,,"['active-learning', 'structure-prediction', 'structure-optimization', 'rep-eng', 'lang-fortran']",,,2021-04-07 09:16:07,2021-04-07 09:16:07.000000,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,, +492,nep-data,,datasets,https://gitlab.com/brucefan1983/nep-data,Data related to the NEP machine-learned potential of GPUMD.,2,False,,"['ml-iap', 'md', 'transport-phenomena']",,,2021-11-22 19:43:01,2021-11-22 19:43:01.000000,,,,9.0,,,1.0,1.0,18.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,, +493,MLDensity_tutorial,,educational,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +494,ofdft_nflows,,ml-dft,https://github.com/ChemAI-Lab/ofdft_nflows,Nomalizing flows for orbita-free DFT.,2,False,,['generative'],ChemAI-Lab/ofdft_nflows,https://github.com/ChemAI-Lab/ofdft_nflows,2023-05-22 23:02:05,2024-09-20 13:42:07.000000,2024-07-19 19:43:15,105.0,,1.0,2.0,9.0,,,10.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +495,SingleNN,,ml-iap,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000000,2021-11-09 00:40:10,17.0,,1.0,2.0,,1.0,,9.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +496,A3MD,,ml-dft,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,,"['rep-learn', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39.000000,2021-12-02 17:10:34,4.0,,1.0,1.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +497,mag-ace,,ml-iap,https://github.com/mttrin93/mag-ace,Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.,2,False,,"['magnetism', 'md', 'lang-fortran']",mttrin93/mag-ace,https://github.com/mttrin93/mag-ace,2023-12-26 19:00:40,2025-05-08 11:16:27.000000,2025-05-08 11:16:26,8.0,1.0,,1.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +498,LAMMPS-style pair potentials with GAP,,educational,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +499,AisNet,,ml-iap,https://github.com/loilisxka/AisNet,A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..,2,False,MIT,,loilisxka/AisNet,https://github.com/loilisxka/AisNet,2022-10-11 05:54:59,2022-10-11 06:02:47.000000,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +500,MALA Tutorial,,educational,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000000,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +501,RuNNer,,ml-iap,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,GPL-3.0,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/ +502,Point Edge Transformer,,rep-learn,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,CC-BY-4.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +503,XElemNet,,xai,https://github.com/KWang1998/XElemNet,Using explainable artificial intelligence (XAI) techniques to analyze ElemNet...,2,False,,"['rep-eng', 'single-paper']",KWang1998/XElemNet,https://github.com/KWang1998/XElemNet,2024-04-29 21:23:24,2024-09-18 06:32:16.000000,2024-09-18 06:32:15,6.0,,,1.0,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +504,tmQM_wB97MV Dataset,,datasets,https://github.com/ulissigroup/tmQM_wB97MV,Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..,1,False,,"['catalysis', 'rep-learn']",ulissigroup/tmqm_wB97MV,https://github.com/ulissigroup/tmQM_wB97MV,2023-07-17 21:40:20,2024-04-09 22:01:26.000000,2024-04-09 22:01:26,17.0,,1.0,2.0,,,2.0,7.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +505,nnp-pre-training,,ml-iap,https://github.com/jla-gardner/nnp-pre-training,Synthetic pre-training for neural-network interatomic potentials.,1,False,,"['pretrained', 'md']",jla-gardner/nnp-pre-training,https://github.com/jla-gardner/nnp-pre-training,2023-07-12 11:58:29,2023-12-19 12:08:14.000000,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35.000,1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +506,MLDensity,,ml-dft,https://github.com/StefanoSanvitoGroup/MLdensity,Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,1,False,,,StefanoSanvitoGroup/MLdensity,https://github.com/StefanoSanvitoGroup/MLdensity,2023-01-31 20:44:45,2025-01-10 15:21:59.000000,2023-02-22 19:25:51,14.0,,,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +507,SphericalNet,,rep-learn,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +508,Wigner Kernels,,math,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,1,False,,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000000,2023-07-08 15:48:37,109.0,,,1.0,,,1.0,2.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +509,kdft,,ml-dft,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,, +510,mlp,,ml-iap,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +511,GitHub topic materials-informatics,,community,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +512,MateriApps,,community,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +513,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/latest-changes.md b/latest-changes.md index a8eb758..92951bf 100644 --- a/latest-changes.md +++ b/latest-changes.md @@ -2,19 +2,19 @@ _Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._ -- NequIP (🥇31 · ⭐ 740 · 📈) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT -- AtomAI (🥈22 · ⭐ 210 · 📈) - Deep and Machine Learning for Microscopy. MIT computer-vision USL experimental-data -- Scikit-Matter (🥈20 · ⭐ 83 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn -- sGDML (🥈19 · ⭐ 150 · 📈) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT -- OpenEquivariance (🥈14 · ⭐ 72 · 📈) - OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product. BSD-3 rep-learn +- NequIP (🥇32 · ⭐ 740 · 📈) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- FAIR Chemistry datasets (🥇30 · ⭐ 1.6K · 📈) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +- fairchem (🥇30 · ⭐ 1.6K · 📈) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +- FAIRChem EquiformerV2 models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Equiformer V2 (eqV2) models. MIT pretrained UIP rep-learn catalysis +- FAIRChem eSEN models (🥇30 · ⭐ 1.6K · 📈) - FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147. MIT pretrained UIP rep-learn catalysis ## 📉 Trending Down _Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._ -- Deep Graph Library (DGL) (🥇36 · ⭐ 14K · 📉) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 -- MatBench Discovery (🥇19 · ⭐ 160 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository -- matsciml (🥈15 · ⭐ 170 · 📉) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking -- CiderPress (🥈8 · ⭐ 12 · 📉) - A high-performance software package for training and evaluating machine-learned XC functionals using the CIDER.. GPL-3.0 ml-functional C-lang -- GDB-9-Ex9 and ORNL_AISD-Ex (🥉5 · ⭐ 8 · 📉) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed +- RDKit (🥇37 · ⭐ 3K · 📉) - BSD-3 C++ cheminformatics +- JAX-DFT (🥇25 · ⭐ 36K · 📉) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 +- cdk (🥇25 · ⭐ 540 · 📉) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java +- TorchANI (🥇23 · ⭐ 500 · 💀) - Accurate Neural Network Potential on PyTorch. MIT +- SMACT (🥇23 · ⭐ 110 · 📉) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics