Best of Atomistic Machine Learning โ๏ธ๐งฌ๐
๐ A ranked list of awesome atomistic machine learning (AML) projects. Updated regularly.
This curated list contains 520 awesome open-source projects with a total of 250K stars grouped into 23 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml.
The current focus of this list is more on simulation data rather than experimental data, and more on materials rather than drug design. Nevertheless, contributions from other fields are warmly welcome!
How to cite. See the button "Cite this repository" on the right side-bar.
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- ๐ฅ๐ฅ๐ฅ Combined project-quality score
- โญ๏ธ Star count from GitHub
- ๐ฃ New project (less than 6 months old)
- ๐ค Inactive project (6 months no activity)
- ๐ Dead project (12 months no activity)
- ๐๐ Project is trending up or down
- โ Project was recently added
- ๐จโ๐ป Contributors count from GitHub
- ๐ Fork count from GitHub
- ๐ Issue count from GitHub
- โฑ๏ธ Last update timestamp on package manager
- ๐ฅ Download count from package manager
- ๐ฆ Number of dependent projects

Projects that focus on enabling active learning, iterative learning schemes for atomistic ML.
DP-GEN (๐ฅ25 ยท โญ 400) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 ML-IAP MD workflows
- [GitHub](https://github.com/deepmodeling/dpgen) (๐จโ๐ป 76 ยท ๐ 200 ยท ๐ฅ 2.1K ยท ๐ฆ 8 ยท ๐ 370 - 3% open ยท โฑ๏ธ 15.09.2026):
git clone https://github.com/deepmodeling/dpgen
- [PyPi](https://pypi.org/project/dpgen) (๐ฅ 510 / month ยท ๐ฆ 2 ยท โฑ๏ธ 17.09.2026):
- [Conda](https://anaconda.org/deepmodeling/dpgen) (๐ฅ 340 ยท โฑ๏ธ 25.03.2025):
conda install -c deepmodeling dpgen
FLARE (๐ฅ19 ยท โญ 360) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP
- [GitHub](https://github.com/mir-group/flare) (๐จโ๐ป 44 ยท ๐ 78 ยท ๐ฅ 10 ยท ๐ฆ 12 ยท ๐ 240 - 14% open ยท โฑ๏ธ 19.09.2026):
git clone https://github.com/mir-group/flare
Bgolearn (๐ฅ17 ยท โญ 160) - [NPJ Com. Mat.] Offical implement of Bgolearn. MIT materials-discovery probabilistic
- [GitHub](https://github.com/Bin-Cao/Bgolearn) (๐จโ๐ป 4 ยท ๐ 24 ยท ๐ฅ 94 ยท โฑ๏ธ 30.08.2026):
git clone https://github.com/Bin-Cao/Bgolearn
- [PyPi](https://pypi.org/project/Bgolearn) (๐ฅ 330 / month ยท โฑ๏ธ 28.08.2026):
DP-GEN2 (๐ฅ17 ยท โญ 43) - 2nd generation of the Deep Potential GENerator. LGPL-3.0 ML-IAP MD workflows
- [GitHub](https://github.com/deepmodeling/dpgen2) (๐จโ๐ป 21 ยท ๐ 36 ยท ๐ฆ 6 ยท ๐ 66 - 48% open ยท โฑ๏ธ 07.09.2026):
git clone https://github.com/deepmodeling/dpgen2
IPSuite (๐ฅ16 ยท โญ 25 ยท ๐ค) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. EPL-2.0 ML-IAP MD workflows HTC FAIR
- [GitHub](https://github.com/zincware/IPSuite) (๐จโ๐ป 9 ยท ๐ 13 ยท ๐ฆ 14 ยท ๐ 180 - 49% open ยท โฑ๏ธ 13.03.2026):
git clone https://github.com/zincware/IPSuite
- [PyPi](https://pypi.org/project/ipsuite) (๐ฅ 300 / month ยท ๐ฆ 5 ยท โฑ๏ธ 20.11.2025):
Show 4 hidden projects...
- flare++ (๐ฅ12 ยท โญ 39 ยท ๐) - A many-body extension of the FLARE code. MIT C++ ML-IAP
- Finetuna (๐ฅ9 ยท โญ 71 ยท ๐) - Active Learning for Machine Learning Potentials. MIT
- ACEHAL (๐ฅ5 ยท โญ 16 ยท ๐) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia
- ALEBREW (๐ฅ4 ยท โญ 21 ยท ๐) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD

Projects that collect atomistic ML resources or foster communication within community.
๐ ACE / GRACE support - Support forum for the Atomic Cluster Expansion (ACE) and extensions.
๐ AI for Science Map - Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..
๐ ASE ecosystem - This is a list of software packages related to ASE or using ASE. md, ml-iap
๐ Atomic Cluster Expansion - Atomic Cluster Expansion (ACE) community homepage.
๐ CrystaLLM - Generate a crystal structure from a composition. language-models generative pretrained transformer
๐ GAP-ML.org community homepage ML-IAP
๐ matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..
๐ Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.
MatBench Discovery (๐ฅ22 ยท โญ 260) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository
- [GitHub](https://github.com/janosh/matbench-discovery) (๐จโ๐ป 36 ยท ๐ 72 ยท ๐ฅ 2.3K ยท ๐ฆ 8 ยท ๐ 79 - 3% open ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/janosh/matbench-discovery
- [PyPi](https://pypi.org/project/matbench-discovery) (๐ฅ 1.9K / month ยท ๐ฆ 2 ยท โฑ๏ธ 11.09.2024):
pip install matbench-discovery
Best-of Machine Learning with Python (๐ฅ21 ยท โญ 24K ยท ๐ค) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
- [GitHub](https://github.com/lukasmasuch/best-of-ml-python) (๐จโ๐ป 57 ยท ๐ 3.1K ยท ๐ 70 - 50% open ยท โฑ๏ธ 22.03.2026):
git clone https://github.com/ml-tooling/best-of-ml-python
OpenML (๐ฅ21 ยท โญ 760) - Open Machine Learning. BSD-3 datasets
- [GitHub](https://github.com/openml/OpenML) (๐จโ๐ป 35 ยท ๐ 130 ยท ๐ 1K - 37% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/openml/OpenML
Graph-based Deep Learning Literature (๐ฅ18 ยท โญ 5.1K) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn
- [GitHub](https://github.com/naganandy/graph-based-deep-learning-literature) (๐จโ๐ป 12 ยท ๐ 770 ยท โฑ๏ธ 20.10.2026):
git clone https://github.com/naganandy/graph-based-deep-learning-literature
ML-PEG (๐ฅ18 ยท โญ 71) - ML Performance and Extrapolation Guide. GPL-3.0 benchmarking viz datasets structure-optimization MD transport-phenomena biomolecules long-range electrostatics
- [GitHub](https://github.com/ddmms/ml-peg) (๐จโ๐ป 44 ยท ๐ 72 ยท ๐ฅ 68 ยท ๐ 410 - 53% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/ddmms/ml-peg
- [PyPi](https://pypi.org/project/ml-peg) (๐ฅ 140 / month ยท โฑ๏ธ 21.10.2026):
Garden (๐ฅ16 ยท โญ 41 ยท ๐ค) - FAIR AI/ML Model Publishing Framework. MIT model-repository
- [GitHub](https://github.com/Garden-AI/garden) (๐จโ๐ป 14 ยท ๐ 5 ยท ๐ฆ 6 ยท ๐ 380 - 3% open ยท โฑ๏ธ 18.03.2026):
git clone https://github.com/Garden-AI/garden
- [PyPi](https://pypi.org/project/garden-ai) (๐ฅ 760 / month ยท โฑ๏ธ 18.03.2026):
GT4SD - Generative Toolkit for Scientific Discovery (๐ฅ15 ยท โญ 380) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository
- [GitHub](https://github.com/GT4SD/gt4sd-core) (๐จโ๐ป 20 ยท ๐ 81 ยท ๐ 120 - 1% open ยท โฑ๏ธ 10.09.2026):
git clone https://github.com/GT4SD/gt4sd-core
AI for Science Resources (๐ฅ13 ยท โญ 800) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
- [GitHub](https://github.com/divelab/AIRS) (๐จโ๐ป 37 ยท ๐ 96 ยท ๐ 33 - 21% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/divelab/AIRS
GNoME Explorer (๐ฅ12 ยท โญ 1.2K) - Graph Networks for Materials Exploration Database. Apache-2 datasets materials-discovery
- [GitHub](https://github.com/google-deepmind/materials_discovery) (๐จโ๐ป 3 ยท ๐ 200 ยท ๐ 37 - 89% open ยท โฑ๏ธ 29.04.2026):
git clone https://github.com/google-deepmind/materials_discovery
Neural-Network-Models-for-Chemistry (๐ฅ12 ยท โญ 200) - A collection of Neural Network Models for chemistry. MIT rep-learn
- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (๐จโ๐ป 3 ยท ๐ 22 ยท ๐ 2 - 50% open ยท โฑ๏ธ 09.04.2026):
git clone https://github.com/Eipgen/Neural-Network-Models-for-Chemistry
Awesome Materials & Chemistry Datasets (๐ฅ11 ยท โญ 400 ยท ๐) - 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) (๐จโ๐ป 11 ยท ๐ 48 ยท ๐ 15 - 26% open ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/blaiszik/awesome-matchem-datasets
Awesome-Scientific-Language-Models (๐ฅ10 ยท โญ 660) - A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (EMNLP24). MIT language-models general-ml pretrained multimodal
- [GitHub](https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models) (๐จโ๐ป 9 ยท ๐ 37 ยท โฑ๏ธ 16.09.2026):
git clone https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models
optimade.science (๐ฅ9 ยท โญ 10) - A sky-scanner Optimade browser-only GUI. MIT datasets
- [GitHub](https://github.com/tilde-lab/optimade.science) (๐จโ๐ป 9 ยท ๐ 4 ยท ๐ 26 - 26% open ยท โฑ๏ธ 06.07.2026):
git clone https://github.com/tilde-lab/optimade.science
Awesome Neural Geometry (๐ฅ8 ยท โญ 1.1K ยท ๐ค) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed educational rep-learn
- [GitHub](https://github.com/neurreps/awesome-neural-geometry) (๐จโ๐ป 16 ยท ๐ 72 ยท โฑ๏ธ 24.02.2026):
git clone https://github.com/neurreps/awesome-neural-geometry
Awesome Neural SBI (๐ฅ8 ยท โญ 170 ยท ๐ค) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning
- [GitHub](https://github.com/smsharma/awesome-neural-sbi) (๐จโ๐ป 7 ยท ๐ 8 ยท ๐ 2 - 50% open ยท โฑ๏ธ 28.01.2026):
git clone https://github.com/smsharma/awesome-neural-sbi
DeepModeling Projects (๐ฅ8 ยท โญ 8) - DeepModeling projects. CC-BY-4.0
- [GitHub](https://github.com/deepmodeling/deepmodeling-projects) (๐จโ๐ป 4 ยท ๐ 3 ยท โฑ๏ธ 10.04.2026):
git clone https://github.com/deepmodeling/deepmodeling-projects
The Collection of Database and Dataset Resources in Materials Science (๐ฅ7 ยท โญ 460) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets
- [GitHub](https://github.com/sedaoturak/data-resources-for-materials-science) (๐จโ๐ป 2 ยท ๐ 63 ยท โฑ๏ธ 17.07.2026):
git clone https://github.com/sedaoturak/data-resources-for-materials-science
Awesome-Crystal-GNNs (๐ฅ7 ยท โญ 120 ยท ๐ค) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. MIT
- [GitHub](https://github.com/kdmsit/Awesome-Crystal-GNNs) (๐จโ๐ป 2 ยท ๐ 13 ยท โฑ๏ธ 09.03.2026):
git clone https://github.com/kdmsit/Awesome-Crystal-GNNs
Charting ML Publications in Science (๐ฅ6 ยท โญ 47 ยท ๐ค) - Literature analysis of ML applications in materials science, chemistry, physics. MIT literature-data general-ml
- [GitHub](https://github.com/blaiszik/ml_publication_charts) (๐จโ๐ป 2 ยท โฑ๏ธ 10.03.2026):
git clone https://github.com/blaiszik/ml_publication_charts
Show 12 hidden projects...
- MatBench (๐ฅ18 ยท โญ 220 ยท ๐) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking model-repository
- Awesome Materials Informatics (๐ฅ11 ยท โญ 530 ยท ๐) - Curated list of known efforts in materials informatics, i.e. in modern materials science. Custom
- MoLFormers UI (๐ฅ9 ยท โญ 420 ยท ๐) - A family of foundation models trained on chemicals. Apache-2 transformer language-models pretrained drug-discovery
- Awesome-Graph-Generation (๐ฅ8 ยท โญ 360 ยท ๐) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn
- AI for Science paper collection (๐ฅ8 ยท โญ 180 ยท ๐) - List the AI for Science papers accepted by top conferences. Apache-2
- A Highly Opinionated List of Open-Source Materials Informatics Resources (๐ฅ8 ยท โญ 160 ยท ๐) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
- MADICES Awesome Interoperability (๐ฅ7 ยท โญ 1) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. MIT datasets
- LAM Crystal Philately competition 2024 (๐ฅ5 ยท โญ 23 ยท ๐) - OpenLAM Challenge crystal structure prediction https://arxiv.org/abs/2501.16358. LGPL-2.1 single-paper datasets structure-prediction materials-discovery ML-IAP UIP
- Geometric-GNNs (๐ฅ4 ยท โญ 120 ยท ๐) - List of Geometric GNNs for 3D atomic systems. Unlicensed datasets educational rep-learn
- Does this material exist? (๐ฅ3 ยท โญ 18 ยท ๐) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery
- GitHub topic materials-informatics (๐ฅ1) - GitHub topic materials-informatics. Unlicensed
- MateriApps (๐ฅ1) - A Portal Site of Materials Science Simulation. Unlicensed

Datasets, databases and trained models for atomistic ML.
๐ Alexandria Materials Database - A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..
๐ Catalysis Hub - A web-platform for sharing data and software for computational catalysis research!.
๐ Citrination Datasets - AI-Powered Materials Data Platform. Open Citrination has been decommissioned.
๐ crystals.ai - Curated datasets for reproducible AI in materials science.
๐ DeepChem Models - DeepChem models on HuggingFace. model-repository pretrained language-models
๐ Graphs of Materials Project 20190401 - The dataset used to train the MEGNet interatomic potential. ML-IAP
๐ HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP
๐ JARVIS-Leaderboard ( โญ 76 ยท ๐) - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational
๐ Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.
๐ Materials Project Trajectory (MPtrj) Dataset - The dataset used to train the CHGNet universal potential. UIP
๐ matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.
๐ MPF.2021.2.8 - The dataset used to train the M3GNet universal potential. UIP
๐ NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..
๐ QM9 Charge Densities and Energies - QM9 molecules calculated with VASP using Atomic Simulation Environment. ML-DFT
๐ QM40 Dataset - A More Realistic QM Dataset for Machine Learning in Molecular Science https://doi.org/10.1038/s41597-024-04206-y. drug-discovery
๐ QMugs dataset - Quantum Mechanical Properties of Drug-like Molecules https://doi.org/10.1038/s41597-022-01390-7. drug-discovery
๐ Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.
๐ sGDML Datasets - MD17, MD22, DFT datasets.
๐ MoleculeNet - A Benchmark for Molecular Machine Learning. benchmarking
๐ ZINC15 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules
๐ ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules
FAIR Chemistry datasets (๐ฅ31 ยท โญ 2.3K) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis
- [GitHub](https://github.com/facebookresearch/fairchem) (๐จโ๐ป 75 ยท ๐ 520 ยท ๐ 620 - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/FAIR-Chem/fairchem
- [PyPi](https://pypi.org/project/fairchem-core) (๐ฅ 19K / month ยท ๐ฆ 95 ยท โฑ๏ธ 22.10.2026):
pip install fairchem-core
Meta Open Materials 2024 (OMat24) Dataset (๐ฅ30 ยท โญ 2.3K) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0
- [GitHub](https://github.com/facebookresearch/fairchem) (๐จโ๐ป 75 ยท ๐ 520 ยท ๐ 620 - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/FAIR-Chem/fairchem
- [PyPi](https://pypi.org/project/fairchem-core) (๐ฅ 19K / month ยท ๐ฆ 95 ยท โฑ๏ธ 22.10.2026):
pip install fairchem-core
MPContribs (๐ฅ24 ยท โญ 42) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
- [GitHub](https://github.com/materialsproject/MPContribs) (๐จโ๐ป 33 ยท ๐ 28 ยท ๐ฆ 61 ยท ๐ 120 - 29% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/materialsproject/MPContribs
- [PyPi](https://pypi.org/project/mpcontribs-client) (๐ฅ 5.3K / month ยท ๐ฆ 7 ยท โฑ๏ธ 09.02.2026):
pip install mpcontribs-client
OPTIMADE Python tools (๐ฅ22 ยท โญ 92) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT
- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (๐จโ๐ป 37 ยท ๐ 56 ยท ๐ 500 - 21% open ยท โฑ๏ธ 06.08.2026):
git clone https://github.com/Materials-Consortia/optimade-python-tools
- [PyPi](https://pypi.org/project/optimade) (๐ฅ 14K / month ยท ๐ฆ 7 ยท โฑ๏ธ 21.06.2026):
- [Conda](https://anaconda.org/conda-forge/optimade) (๐ฅ 210K ยท โฑ๏ธ 06.09.2026):
conda install -c conda-forge optimade
Open Databases Integration for Materials Design (OPTIMADE) (๐ฅ15 ยท โญ 110) - Specification of a common REST API for access to materials databases. CC-BY-4.0
- [GitHub](https://github.com/Materials-Consortia/OPTIMADE) (๐จโ๐ป 24 ยท ๐ 39 ยท ๐ 260 - 29% open ยท โฑ๏ธ 31.08.2026):
git clone https://github.com/Materials-Consortia/OPTIMADE
load-atoms (๐ฅ15 ยท โญ 49 ยท ๐ค) - download and manipulate atomistic datasets. MIT data-structures
- [GitHub](https://github.com/jla-gardner/load-atoms) (๐จโ๐ป 5 ยท ๐ 5 ยท ๐ฆ 8 ยท ๐ 35 - 14% open ยท โฑ๏ธ 25.11.2025):
git clone https://github.com/jla-gardner/load-atoms
- [PyPi](https://pypi.org/project/load-atoms) (๐ฅ 710 / month ยท ๐ฆ 3 ยท โฑ๏ธ 25.11.2025):
QH9 (๐ฅ13 ยท โญ 800) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT
- [GitHub](https://github.com/divelab/AIRS) (๐จโ๐ป 37 ยท ๐ 96 ยท ๐ 33 - 21% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/divelab/AIRS
MatPES (๐ฅ13 ยท โญ 61) - A foundational potential energy dataset for materials. BSD-3 UIP ML-IAP
- [GitHub](https://github.com/materialyzeai/matpes) (๐จโ๐ป 3 ยท ๐ 5 ยท ๐ 10 - 20% open ยท โฑ๏ธ 01.07.2026):
git clone https://github.com/materialsvirtuallab/matpes
- [PyPi](https://pypi.org/project/matpes) (๐ฅ 160 / month ยท ๐ฆ 2 ยท โฑ๏ธ 10.03.2025):
nablaDFT (๐ฅ12 ยท โญ 240 ยท ๐ค) - nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset. MIT ML-DFT ML-WFT drug-discovery ML-IAP benchmarking
- [GitHub](https://github.com/AIRI-Institute/nablaDFT) (๐จโ๐ป 9 ยท ๐ 25 ยท ๐ 28 - 10% open ยท โฑ๏ธ 31.12.2025):
git clone https://github.com/AIRI-Institute/nablaDFT
MPDS API (๐ฅ12 ยท โญ 27) - Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials.. CC-BY-4.0 phase-transition
- [GitHub](https://github.com/mpds-io/mpds-api) (๐จโ๐ป 5 ยท ๐ 7 ยท ๐ 36 - 22% open ยท โฑ๏ธ 23.08.2026):
git clone https://github.com/mpds-io/mpds-api
- [PyPi](https://pypi.org/project/mpds_client) (๐ฅ 310 / month ยท โฑ๏ธ 14.09.2020):
SPICE (๐ฅ11 ยท โญ 210 ยท ๐ค) - A collection of QM data for training potential functions. MIT ML-IAP MD
- [GitHub](https://github.com/openmm/spice-dataset) (๐จโ๐ป 1 ยท ๐ 11 ยท ๐ฅ 450 ยท ๐ 76 - 27% open ยท โฑ๏ธ 25.02.2026):
git clone https://github.com/openmm/spice-dataset
OpenKIM (๐ฅ11 ยท โญ 40 ยท ๐ค) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 model-repository knowledge-base pretrained
- [GitHub](https://github.com/openkim/kim-api) (๐จโ๐ป 27 ยท ๐ 20 ยท ๐ 37 - 40% open ยท โฑ๏ธ 06.03.2026):
git clone https://github.com/openkim/kim-api
OBELiX (๐ฅ9 ยท โญ 64 ยท ๐ค) - 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) (๐จโ๐ป 6 ยท ๐ 9 ยท ๐ 2 - 50% open ยท โฑ๏ธ 27.11.2025):
git clone https://github.com/NRC-Mila/OBELiX
- [PyPi](https://pypi.org/project/obelix-data) (๐ฅ 86 / month ยท โฑ๏ธ 16.05.2025):
polyVERSE (๐ฅ9 ยท โญ 47) - polyVERSE is a comprehensive repository of informatics-ready datasets curated by the Ramprasad Group. Custom soft-matter
- [GitHub](https://github.com/Ramprasad-Group/polyVERSE) (๐จโ๐ป 11 ยท ๐ 8 ยท โฑ๏ธ 23.08.2026):
git clone https://github.com/Ramprasad-Group/polyVERSE
AIS Square (๐ฅ9 ยท โญ 15) - 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) (๐จโ๐ป 8 ยท ๐ 10 ยท ๐ 6 - 83% open ยท โฑ๏ธ 08.10.2026):
git clone https://github.com/deepmodeling/AIS-Square
The Perovskite Database Project (๐ฅ5 ยท โญ 75 ยท ๐ค) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource
- [GitHub](https://github.com/Jesperkemist/perovskitedatabase) (๐จโ๐ป 2 ยท ๐ 26 ยท โฑ๏ธ 20.03.2026):
git clone https://github.com/Jesperkemist/perovskitedatabase
Visual Graph Datasets (๐ฅ5 ยท โญ 5 ยท ๐ค) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn
- [GitHub](https://github.com/aimat-lab/visual_graph_datasets) (๐จโ๐ป 2 ยท ๐ 3 ยท โฑ๏ธ 24.03.2026):
git clone https://github.com/aimat-lab/visual_graph_datasets
Show 17 hidden projects...
- ATOM3D (๐ฅ19 ยท โญ 320 ยท ๐) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking
- OpenQDC (๐ฅ14 ยท โญ 69 ยท ๐) - Repository of Quantum Datasets Publicly Available. CC-BY-4.0
- MoleculeNet Leaderboard (๐ฅ9 ยท โญ 110 ยท ๐) - MIT benchmarking
- Materials Data Facility (MDF) (๐ฅ9 ยท โญ 10 ยท ๐) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2
- 2DMD dataset (๐ฅ9 ยท โญ 8 ยท ๐) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 material-defect
- ANI-1 Dataset (๐ฅ8 ยท โญ 110 ยท ๐) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
- GEOM (๐ฅ7 ยท โญ 260 ยท ๐) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
- SciGlass (๐ฅ7 ยท โญ 17 ยท ๐) - The database contains a vast set of data on the properties of glass materials. MIT
- ANI-1x Datasets (๐ฅ6 ยท โญ 71 ยท ๐) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
- COMP6 Benchmark dataset (๐ฅ6 ยท โญ 42 ยท ๐) - COMP6 Benchmark dataset for ML potentials. MIT
- GDB-9-Ex9 and ORNL_AISD-Ex (๐ฅ5 ยท โญ 11 ยท ๐) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed
- OPTIMADE providers dashboard (๐ฅ5 ยท โญ 2 ยท ๐) - A dashboard of known providers. Unlicensed
- 3DSC Database (๐ฅ4 ยท โญ 30 ยท ๐) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom superconductors materials-discovery
- paper-data-redundancy (๐ฅ4 ยท โญ 11 ยท ๐) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper
- linear-regression-benchmarks (๐ฅ4 ยท โญ 1 ยท ๐) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper
- nep-data (๐ฅ3 ยท โญ 21 ยท ๐) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena
- tmQM_wB97MV Dataset (๐ฅ1 ยท โญ 12 ยท ๐) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn

Projects that focus on providing data structures used in atomistic machine learning.
dpdata (๐ฅ27 ยท โญ 260) - A Python package for manipulating atomistic data of software in computational science. LGPL-3.0
- [GitHub](https://github.com/deepmodeling/dpdata) (๐จโ๐ป 72 ยท ๐ 160 ยท ๐ฆ 160 ยท ๐ 160 - 7% open ยท โฑ๏ธ 12.09.2026):
git clone https://github.com/deepmodeling/dpdata
- [PyPi](https://pypi.org/project/dpdata) (๐ฅ 15K / month ยท ๐ฆ 47 ยท โฑ๏ธ 19.08.2026):
- [Conda](https://anaconda.org/deepmodeling/dpdata) (๐ฅ 510 ยท โฑ๏ธ 25.03.2025):
conda install -c deepmodeling dpdata
Metatensor (๐ฅ23 ยท โญ 110) - 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) (๐จโ๐ป 37 ยท ๐ 28 ยท ๐ฅ 59K ยท ๐ฆ 16 ยท ๐ 270 - 20% open ยท โฑ๏ธ 15.10.2026):
git clone https://github.com/metatensor/metatensor
- [PyPi](https://pypi.org/project/metatensor) (๐ฅ 1.2K / month ยท โฑ๏ธ 26.01.2024):
mp-pyrho (๐ฅ19 ยท โญ 43) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT
- [GitHub](https://github.com/materialsproject/pyrho) (๐จโ๐ป 10 ยท ๐ 10 ยท ๐ฆ 38 ยท ๐ 10 - 70% open ยท โฑ๏ธ 11.05.2026):
git clone https://github.com/materialsproject/pyrho
- [PyPi](https://pypi.org/project/mp-pyrho) (๐ฅ 74K / month ยท ๐ฆ 5 ยท โฑ๏ธ 13.10.2025):
dlpack (๐ฅ16 ยท โญ 1.3K) - common in-memory tensor structure. Apache-2 C++
- [GitHub](https://github.com/dmlc/dlpack) (๐จโ๐ป 36 ยท ๐ 170 ยท ๐ 90 - 28% open ยท โฑ๏ธ 11.08.2026):
git clone https://github.com/dmlc/dlpack

Projects and models that focus on quantities of DFT, such as density functional approximations (ML-DFA), the charge density, density of states, the Hamiltonian, etc.
๐ IKS-PIML - Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning.. neural-operator pinn datasets single-paper
๐ M-OFDFT - Overcoming the Barrier of Orbital-Free Density Functional Theory in Molecular Systems Using Deep Learning.. transformer single-paper
JAX-DFT (๐ฅ26 ยท โญ 39K) - 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) (๐จโ๐ป 890 ยท ๐ 8.4K ยท ๐ 2.3K - 84% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/google-research/google-research
HamGNN (๐ฅ16 ยท โญ 230) - 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) (๐จโ๐ป 9 ยท ๐ 47 ยท ๐ 90 - 82% open ยท โฑ๏ธ 18.09.2026):
git clone https://github.com/QuantumLab-ZY/HamGNN
SALTED (๐ฅ15 ยท โญ 50) - Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response). GPL-3.0
- [GitHub](https://github.com/andreagrisafi/SALTED) (๐จโ๐ป 30 ยท ๐ 10 ยท ๐ 17 - 11% open ยท โฑ๏ธ 30.09.2026):
git clone https://github.com/andreagrisafi/SALTED
CiderPress (๐ฅ15 ยท โญ 29) - 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/cider-dft/CiderPress) (๐จโ๐ป 3 ยท ๐ 5 ยท โฑ๏ธ 27.08.2026):
git clone https://github.com/mir-group/CiderPress
- [PyPi](https://pypi.org/project/ciderpress) (๐ฅ 68 / month ยท โฑ๏ธ 27.08.2026):
QHNet (๐ฅ13 ยท โญ 800) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn
- [GitHub](https://github.com/divelab/AIRS) (๐จโ๐ป 37 ยท ๐ 96 ยท ๐ 33 - 21% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/divelab/AIRS
Q-stack (๐ฅ12 ยท โญ 20) - 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) (๐จโ๐ป 8 ยท ๐ 7 ยท ๐ 57 - 15% open ยท โฑ๏ธ 03.08.2026):
git clone https://github.com/lcmd-epfl/Q-stack
MLDensity (๐ฅ9 ยท โญ 9) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. LGPL-3.0
- [GitHub](https://github.com/StefanoSanvitoGroup/MLdensity) (๐จโ๐ป 4 ยท โฑ๏ธ 05.10.2026):
git clone https://github.com/StefanoSanvitoGroup/MLdensity
Libnxc (๐ฅ8 ยท โญ 21) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
- [GitHub](https://github.com/semodi/libnxc) (๐จโ๐ป 3 ยท ๐ 4 ยท ๐ 16 - 81% open ยท โฑ๏ธ 14.10.2026):
git clone https://github.com/semodi/libnxc
dftio (๐ฅ8 ยท โญ 16 ยท ๐ค) - dftio is to assist machine learning communities to transcript DFT output into a format that is easy to read or used by.. LGPL-3.0 data-structures workflows
- [GitHub](https://github.com/deepmodeling/dftio) (๐จโ๐ป 5 ยท ๐ 14 ยท ๐ 26 - 84% open ยท โฑ๏ธ 18.12.2025):
git clone https://github.com/deepmodeling/dftio
DeepH-E3 (๐ฅ7 ยท โญ 120 ยท ๐ค) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism
- [GitHub](https://github.com/Xiaoxun-Gong/DeepH-E3) (๐จโ๐ป 2 ยท ๐ 29 ยท ๐ 40 - 67% open ยท โฑ๏ธ 27.01.2026):
git clone https://github.com/Xiaoxun-Gong/DeepH-E3
Show 27 hidden projects...
- DM21 (๐ฅ19 ยท โญ 15K ยท ๐) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. Apache-2
- MALA (๐ฅ16 ยท โญ 100 ยท ๐) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3
- DeepH-pack (๐ฅ12 ยท โญ 340 ยท ๐) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0 Julia
- Grad DFT (๐ฅ10 ยท โญ 120 ยท ๐) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
- NeuralXC (๐ฅ10 ยท โญ 39 ยท ๐) - Implementation of a machine learned density functional. BSD-3
- DeePKS-kit (๐ฅ9 ยท โญ 120 ยท ๐) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0 ml-functional
- PROPhet (๐ฅ9 ยท โญ 66 ยท ๐) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
- ACEhamiltonians (๐ฅ9 ยท โญ 17 ยท ๐) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia
- ChargE3Net (๐ฅ8 ยท โญ 78 ยท ๐) - [npj Comp. Mat.] Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn
- Mat2Spec (๐ฅ7 ยท โญ 32 ยท ๐) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings. MIT spectroscopy
- DeepDFT (๐ฅ6 ยท โญ 91 ยท ๐) - Official implementation of DeepDFT model. MIT
- scdp (scalable charge density prediction) (๐ฅ6 ยท โญ 42 ยท ๐) - [NeurIPS 2024] source code for A Recipe for Charge Density Prediction. MIT rep-learn single-paper
- charge-density-models (๐ฅ6 ยท โญ 16 ยท ๐) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn
- KSR-DFT (๐ฅ6 ยท โญ 4 ยท ๐) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2
- xDeepH (๐ฅ5 ยท โญ 41 ยท ๐) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
- InfGCN for Electron Density Estimation (๐ฅ5 ยท โญ 17 ยท ๐) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT rep-learn neural-operator
- rho_learn (๐ฅ5 ยท โญ 4 ยท ๐) - A proof-of-concept workflow for torch-based electron density learning. MIT ML-DFT rep-eng
- ML-DFT (๐ฅ4 ยท โญ 29 ยท ๐) - A package for density functional approximation using machine learning. MIT
- DeepCDP (๐ฅ4 ยท โญ 6 ยท ๐) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
- CSNN (๐ฅ4 ยท โญ 3 ยท ๐) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
- rholearn (๐ฅ4 ยท โญ 3 ยท ๐) - 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
- MALADA (๐ฅ4 ยท โญ 1 ยท ๐) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
- gprep (๐ฅ4 ยท ๐) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
- ofdft_nflows (๐ฅ3 ยท โญ 12 ยท ๐) - Nomalizing flows for orbita-free DFT. Unlicensed generative
- APET (๐ฅ3 ยท โญ 7 ยท ๐) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
- A3MD (๐ฅ2 ยท โญ 8 ยท ๐) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed rep-learn single-paper
- kdft (๐ฅ1 ยท โญ 1 ยท ๐) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed

Tutorials, guides, cookbooks, recipes, etc.
๐ AI for Science 101 community-resource rep-learn
๐ AL4MS 2023 workshop tutorials active-learning
๐ Quantum Chemistry in the Age of Machine Learning - Book, 2022.
COSMO Software Cookbook (๐ฅ14 ยท โญ 56) - A collection of simulation recipes for the atomic-scale modeling of materials and molecules. BSD-3
- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (๐จโ๐ป 23 ยท ๐ 13 ยท ๐ 35 - 31% open ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/lab-cosmo/software-cookbook
Geometric GNN Dojo (๐ฅ12 ยท โญ 530 ยท ๐ค) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
- [GitHub](https://github.com/chaitjo/geometric-gnn-dojo) (๐จโ๐ป 4 ยท ๐ 55 ยท ๐ 9 - 22% open ยท โฑ๏ธ 09.10.2025):
git clone https://github.com/chaitjo/geometric-gnn-dojo
Deep Learning for Molecules and Materials Book (๐ฅ11 ยท โญ 750 ยท ๐ค) - Deep learning for molecules and materials book. Custom
- [GitHub](https://github.com/whitead/dmol-book) (๐จโ๐ป 19 ยท ๐ 130 ยท ๐ 180 - 17% open ยท โฑ๏ธ 20.02.2026):
git clone https://github.com/whitead/dmol-book
AI4Chemistry course (๐ฅ11 ยท โญ 300) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry
- [GitHub](https://github.com/schwallergroup/ai4chem_course) (๐จโ๐ป 10 ยท ๐ 63 ยท ๐ 6 - 50% open ยท โฑ๏ธ 03.04.2026):
git clone https://github.com/schwallergroup/ai4chem_course
DSECOP (๐ฅ9 ยท โญ 56) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
- [GitHub](https://github.com/GDS-Education-Community-of-Practice/DSECOP) (๐จโ๐ป 15 ยท ๐ 26 ยท ๐ 8 - 12% open ยท โฑ๏ธ 13.10.2026):
git clone https://github.com/GDS-Education-Community-of-Practice/DSECOP
iam-notebooks (๐ฅ9 ยท โญ 38) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2
- [GitHub](https://github.com/ceriottm/iam-notebooks) (๐จโ๐ป 7 ยท ๐ 6 ยท โฑ๏ธ 22.05.2026):
git clone https://github.com/ceriottm/iam-notebooks
MLforMaterials (๐ฅ8 ยท โญ 170) - Archived. Course continues as AI for Materials: https://github.com/orgs/AIforMaterials. MIT community-resource general-ml rep-eng materials-discovery
- [GitHub](https://github.com/aronwalsh/MLforMaterials) (๐จโ๐ป 2 ยท ๐ 20 ยท ๐ 4 - 25% open ยท โฑ๏ธ 12.10.2026):
git clone https://github.com/aronwalsh/MLforMaterials
MACE-tutorials (๐ฅ6 ยท โญ 77 ยท ๐ค) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
- [GitHub](https://github.com/ACEsuit/mace-tutorials) (๐จโ๐ป 2 ยท ๐ 18 ยท โฑ๏ธ 22.01.2026):
git clone https://github.com/ilyes319/mace-tutorials
DeepModeling Tutorials (๐ฅ6 ยท โญ 18 ยท ๐ค) - Tutorials for DeepModeling projects. Unlicensed
- [GitHub](https://github.com/deepmodeling/tutorials) (๐จโ๐ป 12 ยท ๐ 24 ยท ๐ 4 - 25% open ยท โฑ๏ธ 11.03.2026):
git clone https://github.com/deepmodeling/tutorials
ML-for-Materials-Science (๐ฅ6 ยท โญ 12) - A comprehensive, interactive learning path for applying machine learning to materials discovery, property prediction,.. MIT general-ml rep-eng XAI
- [GitHub](https://github.com/NabKh/ML-for-Materials-Science) (๐ 2 ยท โฑ๏ธ 17.10.2026):
git clone https://github.com/NabKh/ML-for-Materials-Science
Show 20 hidden projects...
- DeepLearningLifeSciences (๐ฅ12 ยท โญ 420 ยท ๐) - Example code from the book Deep Learning for the Life Sciences. MIT
- Introduction to AI-driven Science on Supercomputers: A Student Training Series (๐ฅ11 ยท โญ 240 ยท ๐ค) - Unlicensed general-ml rep-learn language-models
- jarvis-tools-notebooks (๐ฅ9 ยท โญ 97 ยท ๐) - This repository is no longer maintained. For the latest updates and continued development, please visit:.. NIST
- OPTIMADE Tutorial Exercises (๐ฅ9 ยท โญ 17 ยท ๐) - Tutorial exercises for the OPTIMADE API. MIT datasets
- RDKit Tutorials (๐ฅ8 ยท โญ 310 ยท ๐) - Tutorials to learn how to work with the RDKit. Custom
- BestPractices (๐ฅ8 ยท โญ 200 ยท ๐) - Things that you should (and should not) do in your Materials Informatics research. MIT
- MAChINE (๐ฅ7 ยท โญ 1 ยท ๐) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT
- Applied AI for Materials (๐ฅ6 ยท โญ 87 ยท ๐) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed
- Machine Learning for Materials Hard and Soft (๐ฅ6 ยท โญ 40 ยท ๐) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
- ML for catalysis tutorials (๐ฅ6 ยท โญ 12 ยท ๐) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. MIT
- Data Handling, DoE and Statistical Analysis for Material Chemists (๐ฅ6 ยท โญ 4 ยท ๐) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. GPL-3.0
- AI4Science101 (๐ฅ5 ยท โญ 100 ยท ๐) - AI for Science. Unlicensed
- ML-in-chemistry-101 (๐ฅ4 ยท โญ 91 ยท ๐) - The course materials for Machine Learning in Chemistry 101. Unlicensed
- DSM-CORE (๐ฅ4 ยท โญ 17 ยท ๐) - Data Science for Materials - Collection of Open Educational Resources. Unlicensed
- chemrev-gpr (๐ฅ4 ยท โญ 11 ยท ๐) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
- AI4ChemMat Hands-On Series (๐ฅ4 ยท โญ 1 ยท ๐) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. MPL-2.0
- PiNN Lab (๐ฅ3 ยท โญ 3 ยท ๐) - Material for running a lab session on atomic neural networks. GPL-3.0
- MLDensity_tutorial (๐ฅ2 ยท โญ 12 ยท ๐) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
- LAMMPS-style pair potentials with GAP (๐ฅ2 ยท โญ 4 ยท ๐) - A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD.. Unlicensed ML-IAP MD rep-eng
- MALA Tutorial (๐ฅ2 ยท โญ 2 ยท ๐) - A full MALA hands-on tutorial. Unlicensed

Projects that focus on explainability and model interpretability in atomistic ML.
MEGAN: Multi Explanation Graph Attention Student (๐ฅ9 ยท โญ 12) - Minimal implementation of graph attention student model architecture. MIT rep-learn
- [GitHub](https://github.com/aimat-lab/graph_attention_student) (๐จโ๐ป 2 ยท ๐ 2 ยท ๐ 5 - 60% open ยท โฑ๏ธ 20.10.2026):
git clone https://github.com/aimat-lab/graph_attention_student
Show 3 hidden projects...
- exmol (๐ฅ19 ยท โญ 350 ยท ๐) - Explainer for black box models that predict molecule properties. MIT
- Linear vs blackbox (๐ฅ3 ยท โญ 2 ยท ๐) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng
- XElemNet (๐ฅ2 ยท ๐) - Using explainable artificial intelligence (XAI) techniques to analyze ElemNet... Unlicensed rep-eng single-paper

Projects and models that focus on quantities of electronic structure methods, which do not fit into either of the categories ML-WFT or ML-DFT.
DeePTB (๐ฅ15 ยท โญ 120) - DeePTB: A deep learning package for tight-binding Hamiltonian with ab initio accuracy. LGPL-3.0 ML-DFT
- [GitHub](https://github.com/deepmodeling/DeePTB) (๐จโ๐ป 15 ยท ๐ 34 ยท ๐ฆ 4 ยท ๐ 80 - 47% open ยท โฑ๏ธ 12.10.2026):
git clone https://github.com/deepmodeling/DeePTB
- [PyPi](https://pypi.org/project/dptb) (๐ฅ 220 / month ยท ๐ฆ 2 ยท โฑ๏ธ 20.06.2026):
Show 5 hidden projects...
- QDF for molecule (๐ฅ8 ยท โญ 230 ยท ๐) - Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation.. MIT
- QMLearn (๐ฅ5 ยท โญ 12 ยท ๐) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis... MIT
- q-pac (๐ฅ5 ยท โญ 6 ยท ๐) - Kernel charge equilibration method. MIT electrostatics
- halex (๐ฅ5 ยท โญ 5 ยท ๐) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed excited-states
- e3psi (๐ฅ4 ยท โญ 7 ยท ๐) - Equivariant machine learning library for learning from electronic structures. LGPL-3.0

General tools for atomistic machine learning.
RDKit (๐ฅ39 ยท โญ 3.6K) - BSD-3 C++ cheminformatics
- [GitHub](https://github.com/rdkit/rdkit) (๐จโ๐ป 290 ยท ๐ 1.1K ยท ๐ฆ 320 ยท ๐ 3.8K - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/rdkit/rdkit
- [PyPi](https://pypi.org/project/rdkit) (๐ฅ 4.7M / month ยท ๐ฆ 2.2K ยท โฑ๏ธ 09.10.2026):
- [Conda](https://anaconda.org/rdkit/rdkit) (๐ฅ 2.6M ยท โฑ๏ธ 25.03.2025):
conda install -c rdkit rdkit
DeepChem (๐ฅ33 ยท โญ 7.1K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT
- [GitHub](https://github.com/deepchem/deepchem) (๐จโ๐ป 260 ยท ๐ 2.3K ยท ๐ฆ 670 ยท ๐ 2.5K - 47% open ยท โฑ๏ธ 20.08.2026):
git clone https://github.com/deepchem/deepchem
- [PyPi](https://pypi.org/project/deepchem) (๐ฅ 21K / month ยท ๐ฆ 32 ยท โฑ๏ธ 21.08.2026):
- [Conda](https://anaconda.org/conda-forge/deepchem) (๐ฅ 130K ยท โฑ๏ธ 22.04.2025):
conda install -c conda-forge deepchem
- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (๐ฅ 12K ยท โญ 5 ยท โฑ๏ธ 15.07.2025):
docker pull deepchemio/deepchem
Matminer (๐ฅ28 ยท โญ 620) - Data mining for materials science. Custom
- [GitHub](https://github.com/hackingmaterials/matminer) (๐จโ๐ป 58 ยท ๐ 220 ยท ๐ฆ 500 ยท ๐ 240 - 14% open ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/hackingmaterials/matminer
- [PyPi](https://pypi.org/project/matminer) (๐ฅ 40K / month ยท ๐ฆ 89 ยท โฑ๏ธ 14.04.2026):
- [Conda](https://anaconda.org/conda-forge/matminer) (๐ฅ 130K ยท โฑ๏ธ 06.09.2026):
conda install -c conda-forge matminer
QUIP (๐ฅ25 ยท โญ 410) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran
- [GitHub](https://github.com/libAtoms/QUIP) (๐จโ๐ป 87 ยท ๐ 130 ยท ๐ฅ 1K ยท ๐ฆ 46 ยท ๐ 500 - 23% open ยท โฑ๏ธ 12.05.2026):
git clone https://github.com/libAtoms/QUIP
- [PyPi](https://pypi.org/project/quippy-ase) (๐ฅ 1.5K / month ยท ๐ฆ 9 ยท โฑ๏ธ 30.04.2026):
- [Docker Hub](https://hub.docker.com/r/libatomsquip/quip) (๐ฅ 11K ยท โญ 4 ยท โฑ๏ธ 24.04.2023):
docker pull libatomsquip/quip
Molfeat (๐ฅ24 ยท โญ 240) - molfeat - the hub for all your molecular featurizers. Apache-2 cheminformatics rep-eng rep-learn generative language-models pretrained
- [GitHub](https://github.com/datamol-io/molfeat) (๐จโ๐ป 22 ยท ๐ 31 ยท ๐ฆ 79 ยท ๐ 59 - 15% open ยท โฑ๏ธ 09.09.2026):
git clone https://github.com/datamol-io/molfeat
- [PyPi](https://pypi.org/project/molfeat) (๐ฅ 4.4K / month ยท ๐ฆ 20 ยท โฑ๏ธ 09.09.2026):
- [Conda](https://anaconda.org/conda-forge/molfeat) (๐ฅ 53K ยท โฑ๏ธ 28.09.2026):
conda install -c conda-forge molfeat
MAML (๐ฅ21 ยท โญ 470) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3
- [GitHub](https://github.com/materialyzeai/maml) (๐จโ๐ป 39 ยท ๐ 98 ยท ๐ฆ 17 ยท ๐ 76 - 14% open ยท โฑ๏ธ 27.07.2026):
git clone https://github.com/materialsvirtuallab/maml
- [PyPi](https://pypi.org/project/maml) (๐ฅ 490 / month ยท ๐ฆ 3 ยท โฑ๏ธ 02.04.2025):
Scikit-Matter (๐ฅ20 ยท โญ 98) - 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) (๐จโ๐ป 19 ยท ๐ 27 ยท ๐ฅ 34 ยท ๐ 84 - 23% open ยท โฑ๏ธ 27.08.2026):
git clone https://github.com/scikit-learn-contrib/scikit-matter
- [PyPi](https://pypi.org/project/skmatter) (๐ฅ 11K / month ยท ๐ฆ 7 ยท โฑ๏ธ 27.08.2026):
- [Conda](https://anaconda.org/conda-forge/skmatter) (๐ฅ 10K ยท โฑ๏ธ 29.08.2026):
conda install -c conda-forge skmatter
MLatom (๐ฅ18 ยท โญ 170) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization
- [GitHub](https://github.com/dralgroup/mlatom) (๐จโ๐ป 8 ยท ๐ 26 ยท ๐ 10 - 50% open ยท โฑ๏ธ 12.09.2026):
git clone https://github.com/dralgroup/mlatom
- [PyPi](https://pypi.org/project/mlatom) (๐ฅ 890 / month ยท ๐ฆ 3 ยท โฑ๏ธ 12.09.2026):
MAST-ML (๐ฅ14 ยท โญ 130 ยท ๐ค) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT
- [GitHub](https://github.com/uw-cmg/MAST-ML) (๐จโ๐ป 19 ยท ๐ 62 ยท ๐ฅ 180 ยท ๐ 220 - 14% open ยท โฑ๏ธ 10.10.2025):
git clone https://github.com/uw-cmg/MAST-ML
Artificial Intelligence for Science (AIRS) (๐ฅ13 ยท โญ 800) - 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/divelab/AIRS) (๐จโ๐ป 37 ยท ๐ 96 ยท ๐ 33 - 21% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/divelab/AIRS
Show 14 hidden projects...
- JARVIS-Tools (๐ฅ23 ยท โญ 400 ยท ๐) - About JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom
- AtomAI (๐ฅ18 ยท โญ 230 ยท ๐) - Deep and Machine Learning for Microscopy. MIT computer-vision USL experimental-data
- XenonPy (๐ฅ16 ยท โญ 160 ยท ๐) - XenonPy is a Python Software for Materials Informatics. BSD-3
- QML (๐ฅ15 ยท โญ 210 ยท ๐) - QML: Quantum Machine Learning. MIT
- Automatminer (๐ฅ14 ยท โญ 180 ยท ๐) - An automatic engine for predicting materials properties. Custom autoML
- AMPtorch (๐ฅ11 ยท โญ 62 ยท ๐) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0
- OpenChem (๐ฅ10 ยท โญ 750 ยท ๐) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT
- JAXChem (๐ฅ7 ยท โญ 85 ยท ๐) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT
- uncertainty_benchmarking (๐ฅ7 ยท โญ 44 ยท ๐) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic
- torchchem (๐ฅ7 ยท โญ 38 ยท ๐) - An experimental repo for experimenting with PyTorch models. MIT
- Equisolve (๐ฅ6 ยท โญ 5 ยท ๐) - A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties.. BSD-3 ML-IAP
- quantum-structure-ml (๐ฅ3 ยท โญ 3 ยท ๐) - Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification.. Unlicensed magnetism benchmarking
- ACEatoms (๐ฅ3 ยท โญ 2 ยท ๐) - Generic code for modelling atomic properties using ACE. Custom Julia
- Magpie (๐ฅ3) - Materials Agnostic Platform for Informatics and Exploration (Magpie). MIT Java

Projects that implement generative models for atomistic ML.
GT4SD (๐ฅ17 ยท โญ 380) - 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) (๐จโ๐ป 20 ยท ๐ 81 ยท ๐ 120 - 1% open ยท โฑ๏ธ 10.09.2026):
git clone https://github.com/GT4SD/gt4sd-core
- [PyPi](https://pypi.org/project/gt4sd) (๐ฅ 170 / month ยท โฑ๏ธ 31.07.2026):
PMTransformer (๐ฅ15 ยท โญ 130) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer
- [GitHub](https://github.com/hspark1212/MOFTransformer) (๐จโ๐ป 2 ยท ๐ 23 ยท ๐ฆ 8 ยท ๐ 48 - 4% open ยท โฑ๏ธ 23.05.2026):
git clone https://github.com/hspark1212/MOFTransformer
- [PyPi](https://pypi.org/project/moftransformer) (๐ฅ 200 / month ยท ๐ฆ 1 ยท โฑ๏ธ 20.06.2024):
pip install moftransformer
SiMGen (๐ฅ13 ยท โญ 31) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz
- [GitHub](https://github.com/RokasEl/simgen) (๐จโ๐ป 4 ยท ๐ 5 ยท ๐ฆ 2 ยท ๐ 5 - 20% open ยท โฑ๏ธ 30.08.2026):
git clone https://github.com/RokasEl/simgen
- [PyPi](https://pypi.org/project/simgen) (๐ฅ 25 / month ยท โฑ๏ธ 30.08.2026):
SLICES and MatterGPT (๐ฅ12 ยท โญ 150 ยท ๐ค) - 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) (๐จโ๐ป 2 ยท ๐ 56 ยท ๐ฆ 7 ยท ๐ 17 - 23% open ยท โฑ๏ธ 03.03.2026):
git clone https://github.com/xiaohang007/SLICES
- [PyPi](https://pypi.org/project/slices) (๐ฅ 180 / month ยท ๐ฆ 1 ยท โฑ๏ธ 14.10.2025):
- [Docker Hub](https://hub.docker.com/r/xiaohang07/slices) (๐ฅ 1.1K ยท โญ 1 ยท โฑ๏ธ 14.10.2025):
docker pull xiaohang07/slices
SchNetPack G-SchNet (๐ฅ11 ยท โญ 66 ยท ๐ค) - G-SchNet extension for SchNetPack. MIT
- [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (๐จโ๐ป 3 ยท ๐ 11 ยท โฑ๏ธ 13.11.2025):
git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet
Matcha (๐ฅ8 ยท โญ 37) - Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters.. Unlicensed drug-discovery cheminformatics rep-learn benchmarking
- [GitHub](https://github.com/LigandPro/Matcha) (๐จโ๐ป 3 ยท ๐ 4 ยท ๐ 4 - 50% open ยท โฑ๏ธ 14.10.2026):
git clone https://github.com/LigandPro/Matcha
Show 12 hidden projects...
- MoLeR (๐ฅ15 ยท โญ 330 ยท ๐) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT
- synspace (๐ฅ14 ยท โญ 49 ยท ๐) - Synthesis generative model. MIT
- EDM (๐ฅ9 ยท โญ 580 ยท ๐) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
- G-SchNet (๐ฅ8 ยท โญ 150 ยท ๐) - G-SchNet - a generative model for 3d molecular structures. MIT
- bVAE-IM (๐ฅ8 ยท โญ 14 ยท ๐) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper
- molecular-vae (๐ฅ7 ยท โญ 71 ยท ๐) - Pytorch implementation of the paper Automatic Chemical Design Using a Data-Driven Continuous Representation of.. MIT rep-learn cheminformatics single-paper
- cG-SchNet (๐ฅ7 ยท โญ 70 ยท ๐) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT
- COATI (๐ฅ6 ยท โญ 130 ยท ๐) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery multimodal pretrained rep-learn
- rxngenerator (๐ฅ6 ยท โญ 14 ยท ๐) - A generative model for molecular generation via multi-step chemical reactions. MIT
- MolSLEPA (๐ฅ5 ยท โญ 8 ยท ๐) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI
- Mapping out phase diagrams with generative classifiers (๐ฅ4 ยท โญ 8 ยท ๐) - Repository for our ``Mapping out phase diagrams with generative models paper. MIT phase-transition
- descriptors-inversion (๐ฅ4 ยท โญ 6 ยท ๐) - Local inversion of the chemical environment representations. MIT rep-eng single-paper

Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics.
fairchem (๐ฅ31 ยท โญ 2.3K) - 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) (๐จโ๐ป 75 ยท ๐ 520 ยท ๐ 620 - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/FAIR-Chem/fairchem
- [PyPi](https://pypi.org/project/fairchem-core) (๐ฅ 19K / month ยท ๐ฆ 95 ยท โฑ๏ธ 22.10.2026):
pip install fairchem-core
DeePMD-kit (๐ฅ30 ยท โญ 2.1K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 MD workflows C++
- [GitHub](https://github.com/deepmodeling/deepmd-kit) (๐จโ๐ป 96 ยท ๐ 640 ยท ๐ฅ 79K ยท ๐ฆ 63 ยท ๐ 1.3K - 15% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/deepmodeling/deepmd-kit
- [PyPi](https://pypi.org/project/deepmd-kit) (๐ฅ 17K / month ยท ๐ฆ 22 ยท โฑ๏ธ 19.08.2026):
- [Conda](https://anaconda.org/deepmodeling/deepmd-kit) (๐ฅ 4.4K ยท โฑ๏ธ 25.03.2025):
conda install -c deepmodeling deepmd-kit
- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (๐ฅ 7.7K ยท โญ 1 ยท โฑ๏ธ 22.08.2026):
docker pull deepmodeling/deepmd-kit
NequIP (๐ฅ27 ยท โญ 970) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT
- [GitHub](https://github.com/mir-group/nequip) (๐จโ๐ป 46 ยท ๐ 210 ยท ๐ฆ 59 ยท ๐ 130 - 4% open ยท โฑ๏ธ 31.08.2026):
git clone https://github.com/mir-group/nequip
- [PyPi](https://pypi.org/project/nequip) (๐ฅ 5.6K / month ยท ๐ฆ 29 ยท โฑ๏ธ 01.09.2026):
- [Conda](https://anaconda.org/conda-forge/nequip) (๐ฅ 35K ยท โฑ๏ธ 05.09.2026):
conda install -c conda-forge nequip
TorchANI (๐ฅ27 ยท โญ 560) - TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network.. MIT
- [GitHub](https://github.com/aiqm/torchani) (๐จโ๐ป 24 ยท ๐ 140 ยท ๐ฆ 87 ยท ๐ 180 - 4% open ยท โฑ๏ธ 06.09.2026):
git clone https://github.com/aiqm/torchani
- [PyPi](https://pypi.org/project/torchani) (๐ฅ 4.5K / month ยท ๐ฆ 28 ยท โฑ๏ธ 04.09.2026):
- [Conda](https://anaconda.org/conda-forge/torchani) (๐ฅ 1.6M ยท โฑ๏ธ 22.10.2026):
conda install -c conda-forge torchani
MatCalc (๐ฅ25 ยท โญ 160) - A python library for calculating materials properties from the PES. BSD-3 workflows benchmarking UIP pretrained model-repository
- [GitHub](https://github.com/materialyzeai/matcalc) (๐จโ๐ป 24 ยท ๐ 38 ยท ๐ฆ 27 ยท ๐ 48 - 8% open ยท โฑ๏ธ 08.10.2026):
git clone https://github.com/materialsvirtuallab/matcalc
- [PyPi](https://pypi.org/project/matcalc) (๐ฅ 9.7K / month ยท ๐ฆ 14 ยท โฑ๏ธ 07.10.2026):
MACE (๐ฅ24 ยท โญ 1.4K) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT
- [GitHub](https://github.com/ACEsuit/mace) (๐จโ๐ป 83 ยท ๐ 480 ยท ๐ 690 - 28% open ยท โฑ๏ธ 15.10.2026):
git clone https://github.com/ACEsuit/mace
Metatrain (๐ฅ24 ยท โญ 82) - Train, fine-tune, and manipulate machine learning models for atomistic systems. BSD-3 workflows benchmarking rep-eng rep-learn
- [GitHub](https://github.com/metatensor/metatrain) (๐จโ๐ป 44 ยท ๐ 42 ยท ๐ฅ 120 ยท ๐ฆ 19 ยท ๐ 350 - 33% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/metatensor/metatrain
- [PyPi](https://pypi.org/project/metatrain) (๐ฅ 4.3K / month ยท ๐ฆ 6 ยท โฑ๏ธ 25.09.2026):
janus-core (๐ฅ21 ยท โญ 51) - Tools for machine learnt interatomic potentials. BSD-3 benchmarking workflows structure-optimization MD transport-phenomena
- [GitHub](https://github.com/stfc/janus-core) (๐จโ๐ป 11 ยท ๐ 18 ยท ๐ฅ 280 ยท ๐ฆ 19 ยท ๐ 320 - 17% open ยท โฑ๏ธ 02.10.2026):
git clone https://github.com/stfc/janus-core
- [PyPi](https://pypi.org/project/janus-core) (๐ฅ 4K / month ยท ๐ฆ 9 ยท โฑ๏ธ 02.10.2026):
TorchMD-NET (๐ฅ20 ยท โญ 480) - Training neural network potentials. MIT MD rep-learn transformer pretrained
- [GitHub](https://github.com/torchmd/torchmd-net) (๐จโ๐ป 20 ยท ๐ 98 ยท ๐ฅ 240 ยท ๐ 120 - 26% open ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/torchmd/torchmd-net
- [Conda](https://anaconda.org/conda-forge/torchmd-net) (๐ฅ 1M ยท โฑ๏ธ 19.10.2026):
conda install -c conda-forge torchmd-net
Autoplex (๐ฅ18 ยท โญ 160) - Code for automated fitting of machine learned interatomic potentials. GPL-3.0 benchmarking workflows
- [GitHub](https://github.com/autoatml/autoplex) (๐จโ๐ป 18 ยท ๐ 28 ยท ๐ฆ 2 ยท ๐ 150 - 22% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/autoatml/autoplex
- [PyPi](https://pypi.org/project/autoplex) (๐ฅ 100 / month ยท โฑ๏ธ 17.08.2026):
KLIFF (๐ฅ18 ยท โญ 40 ยท ๐ค) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows
- [GitHub](https://github.com/openkim/kliff) (๐จโ๐ป 14 ยท ๐ 21 ยท ๐ฆ 4 ยท ๐ 56 - 41% open ยท โฑ๏ธ 28.02.2026):
git clone https://github.com/openkim/kliff
- [PyPi](https://pypi.org/project/kliff) (๐ฅ 82 / month ยท โฑ๏ธ 11.04.2025):
- [Conda](https://anaconda.org/conda-forge/kliff) (๐ฅ 260K ยท โฑ๏ธ 06.09.2026):
conda install -c conda-forge kliff
Allegro (๐ฅ17 ยท โญ 500) - Allegro is a code for building highly scalable E(3)-equivariant interatomic potentials. MIT
- [GitHub](https://github.com/mir-group/allegro) (๐จโ๐ป 10 ยท ๐ 75 ยท ๐ฆ 5 ยท ๐ 51 - 1% open ยท โฑ๏ธ 28.05.2026):
git clone https://github.com/mir-group/allegro
Graph-PES (๐ฅ16 ยท โญ 130) - train and use graph-based ML models of potential energy surfaces. MIT rep-learn UIP MD pretrained
- [GitHub](https://github.com/vldgroup/graph-pes) (๐จโ๐ป 8 ยท ๐ 16 ยท ๐ฆ 3 ยท ๐ 20 - 30% open ยท โฑ๏ธ 22.09.2026):
git clone https://github.com/jla-gardner/graph-pes
- [PyPi](https://pypi.org/project/graph-pes) (๐ฅ 1.1K / month ยท ๐ฆ 2 ยท โฑ๏ธ 22.09.2026):
Pacemaker (๐ฅ16 ยท โญ 120) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom
- [GitHub](https://github.com/ICAMS/python-ace) (๐จโ๐ป 9 ยท ๐ 27 ยท ๐ 75 - 45% open ยท โฑ๏ธ 04.09.2026):
git clone https://github.com/ICAMS/python-ace
- [PyPi](https://pypi.org/project/python-ace) (๐ฅ 210 / month ยท โฑ๏ธ 04.09.2026):
wfl (๐ฅ16 ยท โญ 43) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC
- [GitHub](https://github.com/libAtoms/workflow) (๐จโ๐ป 21 ยท ๐ 21 ยท ๐ฆ 6 ยท ๐ 170 - 42% open ยท โฑ๏ธ 22.07.2026):
git clone https://github.com/libAtoms/workflow
apax (๐ฅ16 ยท โญ 38) - A flexible and performant framework for training machine learning potentials. MIT
- [GitHub](https://github.com/apax-hub/apax) (๐จโ๐ป 11 ยท ๐ 8 ยท ๐ฆ 7 ยท ๐ 180 - 17% open ยท โฑ๏ธ 01.06.2026):
git clone https://github.com/apax-hub/apax
- [PyPi](https://pypi.org/project/apax) (๐ฅ 130 / month ยท ๐ฆ 2 ยท โฑ๏ธ 18.03.2026):
aiida-mlip (๐ฅ16 ยท โญ 26) - machine learning interatomic potentials aiida plugin. BSD-3 workflows structure-optimization MD
- [GitHub](https://github.com/stfc/aiida-mlip) (๐จโ๐ป 10 ยท ๐ 14 ยท ๐ฅ 61 ยท ๐ฆ 4 ยท ๐ 160 - 42% open ยท โฑ๏ธ 02.10.2026):
git clone https://github.com/stfc/aiida-mlip
- [PyPi](https://pypi.org/project/aiida-mlip) (๐ฅ 340 / month ยท โฑ๏ธ 17.09.2026):
PiNN (๐ฅ15 ยท โญ 130) - A Python library for building atomic neural networks. BSD-3
- [GitHub](https://github.com/Teoroo-CMC/PiNN) (๐จโ๐ป 9 ยท ๐ 41 ยท ๐ 7 - 14% open ยท โฑ๏ธ 17.09.2026):
git clone https://github.com/Teoroo-CMC/PiNN
- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (๐ฅ 1.2K ยท โฑ๏ธ 17.09.2026):
MLIPX - Machine-Learned Interatomic Potential eXploration (๐ฅ15 ยท โญ 110 ยท ๐ค) - Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned.. MIT benchmarking viz workflows
- [GitHub](https://github.com/basf/mlipx) (๐จโ๐ป 5 ยท ๐ 8 ยท ๐ฆ 7 ยท ๐ 18 - 33% open ยท โฑ๏ธ 16.10.2025):
git clone https://github.com/basf/mlipx
- [PyPi](https://pypi.org/project/mlipx) (๐ฅ 2.4K / month ยท ๐ฆ 4 ยท โฑ๏ธ 09.06.2025):
NNPOps (๐ฅ15 ยท โญ 100 ยท ๐ค) - High-performance operations for neural network potentials. MIT MD C++
- [GitHub](https://github.com/openmm/NNPOps) (๐จโ๐ป 11 ยท ๐ 18 ยท ๐ 67 - 41% open ยท โฑ๏ธ 04.02.2026):
git clone https://github.com/openmm/NNPOps
- [Conda](https://anaconda.org/conda-forge/nnpops) (๐ฅ 840K ยท โฑ๏ธ 17.06.2026):
conda install -c conda-forge nnpops
Neural Force Field (๐ฅ14 ยท โญ 290 ยท ๐ค) - Neural Network Force Field based on PyTorch. MIT pretrained
- [GitHub](https://github.com/learningmatter-mit/NeuralForceField) (๐จโ๐ป 45 ยท ๐ 61 ยท ๐ 23 - 21% open ยท โฑ๏ธ 10.02.2026):
git clone https://github.com/learningmatter-mit/NeuralForceField
calorine (๐ฅ13 ยท โญ 13) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom
- [PyPi](https://pypi.org/project/calorine) (๐ฅ 2.4K / month ยท ๐ฆ 12 ยท โฑ๏ธ 21.09.2026):
- [GitLab](https://gitlab.com/materials-modeling/calorine) (๐ 6 ยท ๐ 150 - 6% open ยท โฑ๏ธ 21.09.2026):
git clone https://gitlab.com/materials-modeling/calorine
DeepMD-GNN (๐ฅ12 ยท โญ 57) - DeePMD-kit plugin for various graph neural network models. LGPL-3.0 rep-learn MD UIP C++
- [GitHub](https://github.com/deepmodeling/deepmd-gnn) (๐จโ๐ป 10 ยท ๐ 10 ยท ๐ 30 - 20% open ยท โฑ๏ธ 16.09.2026):
git clone https://github.com/deepmodeling/deepmd-gnn
TurboGAP (๐ฅ12 ยท โญ 22) - The TurboGAP code. Custom Fortran
- [GitHub](https://github.com/mcaroba/turbogap) (๐จโ๐ป 12 ยท ๐ 14 ยท ๐ 12 - 25% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/mcaroba/turbogap
MACE-Jax (๐ฅ11 ยท โญ 110) - Equivariant machine learning interatomic potentials in JAX. MIT
- [GitHub](https://github.com/ACEsuit/mace-jax) (๐จโ๐ป 9 ยท ๐ 28 ยท ๐ 12 - 50% open ยท โฑ๏ธ 10.08.2026):
git clone https://github.com/ACEsuit/mace-jax
tinker-hp (๐ฅ11 ยท โญ 100) - Tinker-HP: High-Performance Massively Parallel Tinker for CPUs & GPUs. Custom
- [GitHub](https://github.com/TinkerTools/tinker-hp) (๐จโ๐ป 13 ยท ๐ 27 ยท ๐ 29 - 24% open ยท โฑ๏ธ 15.10.2026):
git clone https://github.com/TinkerTools/tinker-hp
Asparagus (๐ฅ11 ยท โญ 13) - 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) (๐จโ๐ป 11 ยท ๐ 6 ยท โฑ๏ธ 05.10.2026):
git clone https://github.com/MMunibas/Asparagus
MLXDM (๐ฅ10 ยท โญ 9) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. MIT long-range
- [GitHub](https://github.com/RowleyGroup/MLXDM) (๐จโ๐ป 8 ยท ๐ 2 ยท โฑ๏ธ 01.09.2026):
git clone https://github.com/RowleyGroup/MLXDM
PyNEP (๐ฅ9 ยท โญ 71 ยท ๐ค) - A python interface of the machine learning potential NEP used in GPUMD. MIT
- [GitHub](https://github.com/bigd4/PyNEP) (๐จโ๐ป 10 ยท ๐ 19 ยท ๐ 14 - 42% open ยท โฑ๏ธ 27.10.2025):
git clone https://github.com/bigd4/PyNEP
GAP (๐ฅ9 ยท โญ 49) - Gaussian Approximation Potential (GAP). Custom
- [GitHub](https://github.com/libAtoms/GAP) (๐จโ๐ป 13 ยท ๐ 21 ยท โฑ๏ธ 05.07.2026):
git clone https://github.com/libAtoms/GAP
ALF (๐ฅ9 ยท โญ 47) - An open-source active learning framework for training machine-learned interatomic potentials. Custom active-learning
- [GitHub](https://github.com/lanl/ALF) (๐จโ๐ป 11 ยท ๐ 18 ยท ๐ 2 - 50% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/lanl/alf
ACEfit (๐ฅ9 ยท โญ 8 ยท ๐ค) - MIT Julia
- [GitHub](https://github.com/ACEsuit/ACEfit.jl) (๐จโ๐ป 10 ยท ๐ 8 ยท ๐ 61 - 39% open ยท โฑ๏ธ 20.03.2026):
git clone https://github.com/ACEsuit/ACEfit.jl
EquiformerV2 (๐ฅ8 ยท โญ 350) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT pretrained UIP rep-learn
- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (๐จโ๐ป 2 ยท ๐ 49 ยท ๐ 27 - 66% open ยท โฑ๏ธ 10.04.2026):
git clone https://github.com/atomicarchitects/equiformer_v2
MatML (๐ฅ5 ยท โญ 9) - 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/materialyzeai/matml) (๐จโ๐ป 2 ยท ๐ 2 ยท โฑ๏ธ 04.05.2026):
git clone https://github.com/materialsvirtuallab/matml
- [Docker Hub](https://hub.docker.com/r/materialsvirtuallab/matml) (๐ฅ 1.2K ยท โญ 2 ยท โฑ๏ธ 08.04.2025):
docker pull materialsvirtuallab/matml
Show 44 hidden projects...
- MEGNet (๐ฅ20 ยท โญ 560 ยท ๐) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3 multifidelity
- sGDML (๐ฅ16 ยท โญ 170 ยท ๐) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT
- Ultra-Fast Force Fields (UF3) (๐ฅ16 ยท โญ 74 ยท ๐) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2
- n2p2 (๐ฅ14 ยท โญ 240 ยท ๐) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++
- PyXtalFF (๐ฅ14 ยท โญ 93 ยท ๐) - Machine Learning Interatomic Potential Predictions. MIT
- TensorMol (๐ฅ12 ยท โญ 280 ยท ๐) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
- ANI-1 (๐ฅ12 ยท โญ 230 ยท ๐) - ANI-1 neural net potential with python interface (ASE). MIT
- So3krates (MLFF) (๐ฅ12 ยท โญ 140 ยท ๐) - Build neural networks for machine learning force fields with JAX. MIT
- SIMPLE-NN (๐ฅ12 ยท โญ 48 ยท ๐) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
- DimeNet (๐ฅ9 ยท โญ 360 ยท ๐) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom
- SchNet (๐ฅ9 ยท โญ 300 ยท ๐) - SchNet - a deep learning architecture for quantum chemistry. MIT
- GemNet (๐ฅ9 ยท โญ 220 ยท ๐) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom
- ACE.jl (๐ฅ9 ยท โญ 67 ยท ๐) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia
- Point Edge Transformer (PET) (๐ฅ9 ยท โญ 37 ยท ๐) - Point Edge Transformer. MIT rep-learn transformer
- ACE1.jl (๐ฅ9 ยท โญ 24 ยท ๐) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia
- NNsforMD (๐ฅ9 ยท โญ 11 ยท ๐) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT
- CCS_fit (๐ฅ9 ยท โญ 10 ยท ๐) - Curvature Constrained Splines. GPL-3.0
- AIMNet (๐ฅ8 ยท โญ 110 ยท ๐) - Atoms In Molecules Neural Network Potential. MIT single-paper
- SIMPLE-NN v2 (๐ฅ8 ยท โญ 45 ยท ๐) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0
- Atomistic Adversarial Attacks (๐ฅ8 ยท โญ 41 ยท ๐) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic
- MEGNetSparse (๐ฅ8 ยท โญ 5 ยท ๐) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
- PhysNet (๐ฅ7 ยท โญ 120 ยท ๐) - Code for training PhysNet models. MIT electrostatics
- SNAP (๐ฅ7 ยท โญ 40 ยท ๐) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
- MLIP-3 (๐ฅ6 ยท โญ 27 ยท ๐) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++
- testing-framework (๐ฅ6 ยท โญ 11 ยท ๐) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. Unlicensed benchmarking
- PANNA (๐ฅ6 ยท โญ 11 ยท ๐) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking
- Alchemical learning (๐ฅ6 ยท โญ 3 ยท ๐) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3 rep-eng Defects & Disorder
- BPNET (๐ฅ6 ยท โญ 3 ยท ๐) - Fast Behler-Parrinello type neural networks in Fortran2008. MIT rep-eng Fortran
- ACE1Pack.jl (๐ฅ6 ยท โญ 1 ยท ๐) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT Julia
- glp (๐ฅ5 ยท โญ 28 ยท ๐) - tools for graph-based machine-learning potentials in jax. MIT
- NequIP-JAX (๐ฅ5 ยท โญ 25 ยท ๐) - JAX implementation of the NequIP interatomic potential. Unlicensed
- Allegro-Legato (๐ฅ5 ยท โญ 21 ยท ๐) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD
- TensorPotential (๐ฅ5 ยท โญ 13 ยท ๐) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom
- GN-MM (๐ฅ5 ยท โญ 12 ยท ๐) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. MIT active-learning MD rep-eng magnetism
- PeriodicPotentials (๐ฅ4 ยท ๐) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
- Allegro-JAX (๐ฅ3 ยท โญ 22 ยท ๐) - JAX implementation of the Allegro interatomic potential. MIT
- ACE Workflows (๐ฅ3 ยท ๐) - Workflow Examples for ACE Models. Unlicensed Julia workflows
- PyFLAME (๐ฅ3 ยท ๐) - An automated approach for developing neural network interatomic potentials with FLAME.. Unlicensed active-learning structure-prediction structure-optimization rep-eng Fortran
- SingleNN (๐ฅ2 ยท โญ 9 ยท ๐) - An efficient package for training and executing neural-network interatomic potentials. Unlicensed C++
- mag-ace (๐ฅ2 ยท โญ 8 ยท ๐) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package. Unlicensed magnetism MD Fortran
- AisNet (๐ฅ2 ยท โญ 3 ยท ๐) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.. MIT
- RuNNer (๐ฅ2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran
- nnp-pre-training (๐ฅ1 ยท โญ 7 ยท ๐) - Synthetic pre-training for neural-network interatomic potentials. Unlicensed pretrained MD
- mlp (๐ฅ1 ยท โญ 1 ยท ๐) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia

Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML.
๐ MaCBench Leaderboard - Leaderboard for multimodal language models for chemistry & materials research. community-resource benchmarking datasets
paper-qa (๐ฅ19 ยท โญ 9K) - LLM Chain for answering questions from docs. Unlicensed ai-agent
- [GitHub]() (๐ 910):
git clone https://github.com/whitead/paper-qa
- [PyPi](https://pypi.org/project/paper-qa) (๐ฅ 65K / month ยท ๐ฆ 29 ยท โฑ๏ธ 12.08.2026):
MolSkill (๐ฅ10 ยท โญ 120 ยท ๐) - Extracting medicinal chemistry intuition via preference machine learning. MIT drug-discovery recommender
- [GitHub](https://github.com/microsoft/molskill) (๐จโ๐ป 5 ยท ๐ 14 ยท ๐ 6 - 33% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/microsoft/molskill
- [Conda](https://anaconda.org/msr-ai4science/molskill) (๐ฅ 570 ยท โฑ๏ธ 25.03.2025):
conda install -c msr-ai4science molskill
LLaMP (๐ฅ8 ยท โญ 100 ยท ๐ค) - [EMNLP 25] A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials.. BSD-3 multimodal RAG materials-discovery pretrained JavaScript Python
- [GitHub](https://github.com/chiang-yuan/llamp) (๐จโ๐ป 6 ยท ๐ 15 ยท ๐ 25 - 32% open ยท โฑ๏ธ 11.11.2025):
git clone https://github.com/chiang-yuan/llamp
LLM-Prop (๐ฅ7 ยท โญ 58 ยท ๐ค) - A repository for the LLM-Prop implementation. MIT
- [GitHub](https://github.com/vertaix/LLM-Prop) (๐จโ๐ป 7 ยท ๐ 11 ยท ๐ 3 - 66% open ยท โฑ๏ธ 31.01.2026):
git clone https://github.com/vertaix/LLM-Prop
Show 20 hidden projects...
- ChemDataExtractor (๐ฅ17 ยท โญ 370 ยท ๐) - Automatically extract chemical information from scientific documents. MIT literature-data
- OpenBioML ChemNLP (๐ฅ17 ยท โญ 180 ยท ๐) - ChemNLP project. MIT datasets
- ChemBench (๐ฅ17 ยท โญ 150 ยท ๐) - How good are LLMs at chemistry?. MIT benchmarking multimodal
- ChemCrow (๐ฅ15 ยท โญ 970 ยท ๐) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent
- mat2vec (๐ฅ12 ยท โญ 640 ยท ๐) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn
- gptchem (๐ฅ12 ยท โญ 260 ยท ๐) - Use GPT-3 to solve chemistry problems. MIT
- ChatMOF (๐ฅ11 ยท โญ 120 ยท ๐) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative
- AtomGPT (๐ฅ11 ยท โญ 50 ยท ๐) - https://atomgpt.org. Custom generative pretrained transformer
- nlcc (๐ฅ11 ยท โญ 46 ยท ๐) - Natural language computational chemistry command line interface. MIT single-paper
- NIST ChemNLP (๐ฅ11 ยท โญ 28 ยท ๐) - chemnlp. MIT literature-data
- MoLFormer (๐ฅ9 ยท โญ 420 ยท ๐) - Repository for MolFormer. Apache-2 transformer pretrained drug-discovery
- chemlift (๐ฅ7 ยท โญ 46 ยท ๐) - Language-interfaced fine-tuning for chemistry. MIT
- crystal-text-llm (๐ฅ6 ยท โญ 120 ยท ๐) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery
- LLM4Chem (๐ฅ6 ยท โญ 120 ยท ๐) - Official code repo for the paper LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,.. MIT cheminformatics datasets
- BERT-PSIE-TC (๐ฅ6 ยท โญ 16 ยท ๐) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism
- SciBot (๐ฅ5 ยท โญ 63 ยท ๐) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed ai-agent
- Cephalo (๐ฅ5 ยท โญ 13 ยท ๐) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained
- MAPI_LLM (๐ฅ5 ยท โญ 9 ยท ๐) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT ai-agent dataset
- CatBERTa (๐ฅ4 ยท โญ 28 ยท ๐) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis
- ChemDataWriter (๐ฅ3 ยท โญ 14 ยท ๐) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data

Projects that implement materials discovery methods using atomistic ML.
SMACT (๐ฅ26 ยท โญ 150) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics
- [GitHub](https://github.com/WMD-group/SMACT) (๐จโ๐ป 53 ยท ๐ 33 ยท ๐ฆ 89 ยท ๐ 66 - 1% open ยท โฑ๏ธ 20.10.2026):
git clone https://github.com/WMD-group/SMACT
- [PyPi](https://pypi.org/project/smact) (๐ฅ 18K / month ยท ๐ฆ 27 ยท โฑ๏ธ 24.09.2026):
- [Conda](https://anaconda.org/conda-forge/smact) (๐ฅ 16K ยท โฑ๏ธ 24.09.2026):
conda install -c conda-forge smact
MatterGen (๐ฅ17 ยท โญ 1.9K) - 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) (๐จโ๐ป 14 ยท ๐ 350 ยท ๐ 150 - 5% open ยท โฑ๏ธ 27.08.2026):
git clone https://github.com/microsoft/mattergen
CARE (๐ฅ15 ยท โญ 94) - Automated creation and manipulation of chemical reaction networks (CRNs) in heterogeneous catalysis, powered by state-.. MIT catalysis cheminformatics ML-IAP Julia
- [GitHub](https://github.com/LopezGroup-ICIQ/care) (๐จโ๐ป 5 ยท ๐ 14 ยท ๐ฅ 8 ยท ๐ 2 - 50% open ยท โฑ๏ธ 28.09.2026):
git clone https://github.com/LopezGroup-ICIQ/care
- [PyPi](https://pypi.org/project/care-crn) (๐ฅ 160 / month ยท โฑ๏ธ 10.09.2026):
BOSS (๐ฅ13 ยท โญ 28) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic
- [PyPi](https://pypi.org/project/aalto-boss) (๐ฅ 370 / month ยท โฑ๏ธ 03.09.2026):
- [GitLab](https://gitlab.com/cest-group/boss) (๐ 16 ยท ๐ 43 - 18% open ยท โฑ๏ธ 03.09.2026):
git clone https://gitlab.com/cest-group/boss
Materials Discovery: GNoME (๐ฅ12 ยท โญ 1.2K) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. Apache-2 UIP datasets rep-learn proprietary
- [GitHub](https://github.com/google-deepmind/materials_discovery) (๐จโ๐ป 3 ยท ๐ 200 ยท ๐ 37 - 89% open ยท โฑ๏ธ 29.04.2026):
git clone https://github.com/google-deepmind/materials_discovery
aviary (๐ฅ12 ยท โญ 63) - The Wren sits on its Roost in the Aviary. MIT
- [GitHub](https://github.com/CompRhys/aviary) (๐จโ๐ป 6 ยท ๐ 13 ยท ๐ฆ 1 ยท ๐ 35 - 11% open ยท โฑ๏ธ 12.04.2026):
git clone https://github.com/CompRhys/aviary
AGOX (๐ฅ10 ยท โญ 19 ยท ๐ค) - 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) (๐ฅ 1.1K / month ยท ๐ฆ 3 ยท โฑ๏ธ 04.02.2026):
- [GitLab](https://gitlab.com/agox/agox) (๐ 8 ยท ๐ 29 - 31% open ยท โฑ๏ธ 04.02.2026):
git clone https://gitlab.com/agox/agox
Show 7 hidden projects...
- Computational Autonomy for Materials Discovery (CAMD) (๐ฅ7 ยท โญ 1 ยท ๐) - Agent-based sequential learning software for materials discovery. Apache-2
- MAGUS (๐ฅ5 ยท โญ 120 ยท ๐) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning
- CSPML (crystal structure prediction with machine learning-based element substitution) (๐ฅ5 ยท โญ 29 ยท ๐) - Original implementation of CSPML. MIT structure-prediction
- SPINNER (๐ฅ4 ยท โญ 15 ยท ๐) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction
- ML-atomate (๐ฅ4 ยท โญ 7 ยท ๐) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows
- closed-loop-acceleration-benchmarks (๐ฅ4 ยท ๐) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
- sl_discovery (๐ฅ3 ยท โญ 5 ยท ๐) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper

Projects that implement mathematical objects used in atomistic machine learning.
cuEquivariance (๐ฅ23 ยท โญ 440 ยท ๐) - 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) (๐จโ๐ป 16 ยท ๐ 44 ยท ๐ 93 - 26% open ยท โฑ๏ธ 16.10.2026):
git clone https://github.com/NVIDIA/cuEquivariance
- [PyPi](https://pypi.org/project/cuequivariance) (๐ฅ 190K / month ยท ๐ฆ 35 ยท โฑ๏ธ 15.10.2026):
pip install cuequivariance
- [Conda](https://anaconda.org/conda-forge/cuequivariance) (๐ฅ 32K ยท โฑ๏ธ 16.10.2026):
conda install -c conda-forge cuequivariance
OpenEquivariance (๐ฅ23 ยท โญ 170) - OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product. BSD-3 rep-learn
- [GitHub](https://github.com/PASSIONLab/OpenEquivariance) (๐จโ๐ป 7 ยท ๐ 17 ยท ๐ฆ 8 ยท ๐ 42 - 9% open ยท โฑ๏ธ 27.09.2026):
git clone https://github.com/PASSIONLab/OpenEquivariance
- [PyPi](https://pypi.org/project/openequivariance) (๐ฅ 72K / month ยท ๐ฆ 6 ยท โฑ๏ธ 11.09.2026):
pip install openequivariance
KFAC-JAX (๐ฅ21 ยท โญ 330) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2
- [GitHub](https://github.com/google-deepmind/kfac-jax) (๐จโ๐ป 22 ยท ๐ 33 ยท ๐ฆ 16 ยท ๐ 38 - 63% open ยท โฑ๏ธ 07.10.2026):
git clone https://github.com/google-deepmind/kfac-jax
- [PyPi](https://pypi.org/project/kfac-jax) (๐ฅ 6.6K / month ยท ๐ฆ 2 ยท โฑ๏ธ 25.02.2026):
SpheriCart (๐ฅ19 ยท โญ 100) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT
- [GitHub](https://github.com/lab-cosmo/sphericart) (๐จโ๐ป 15 ยท ๐ 17 ยท ๐ฅ 1.4K ยท ๐ฆ 12 ยท ๐ 52 - 17% open ยท โฑ๏ธ 14.09.2026):
git clone https://github.com/lab-cosmo/sphericart
- [PyPi](https://pypi.org/project/sphericart) (๐ฅ 4.2K / month ยท ๐ฆ 2 ยท โฑ๏ธ 14.09.2026):
Polynomials4ML.jl (๐ฅ12 ยท โญ 15) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia
- [GitHub](https://github.com/ACEsuit/Polynomials4ML.jl) (๐จโ๐ป 12 ยท ๐ 7 ยท ๐ 62 - 12% open ยท โฑ๏ธ 17.06.2026):
git clone https://github.com/ACEsuit/Polynomials4ML.jl
GElib (๐ฅ10 ยท โญ 27) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0 C++
- [GitHub](https://github.com/risi-kondor/GElib) (๐จโ๐ป 6 ยท ๐ 3 ยท ๐ 8 - 50% open ยท โฑ๏ธ 20.08.2026):
git clone https://github.com/risi-kondor/GElib
COSMO Toolbox (๐ฅ6 ยท โญ 8) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++
- [GitHub](https://github.com/lab-cosmo/toolbox) (๐จโ๐ป 10 ยท ๐ 7 ยท โฑ๏ธ 01.04.2026):
git clone https://github.com/lab-cosmo/toolbox
cnine (๐ฅ6 ยท โญ 5) - Cnine tensor library. Unlicensed C++
- [GitHub](https://github.com/risi-kondor/cnine) (๐จโ๐ป 8 ยท ๐ 4 ยท ๐ 2 - 50% open ยท โฑ๏ธ 15.10.2026):
git clone https://github.com/risi-kondor/cnine
Show 6 hidden projects...
- gpax (๐ฅ17 ยท โญ 240 ยท ๐) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning
- lie-nn (๐ฅ9 ยท โญ 39 ยท ๐) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn
- LapJAX (๐ฅ8 ยท โญ 78 ยท ๐) - A JAX based package designed for efficient second order operators (e.g., laplacian) computation. MIT
- EquivariantOperators.jl (๐ฅ6 ยท โญ 18 ยท ๐) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT Julia
- torch_spex (๐ฅ3 ยท โญ 2 ยท ๐) - Spherical expansions in PyTorch. Unlicensed
- Wigner Kernels (๐ฅ1 ยท โญ 2) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking

Projects that simplify the integration of molecular dynamics and atomistic machine learning.
JAX-MD (๐ฅ27 ยท โญ 1.5K) - Differentiable, Hardware Accelerated, Molecular Dynamics. Apache-2
- [GitHub](https://github.com/jax-md/jax-md) (๐จโ๐ป 54 ยท ๐ 250 ยท ๐ฆ 100 ยท ๐ 200 - 24% open ยท โฑ๏ธ 18.08.2026):
git clone https://github.com/jax-md/jax-md
- [PyPi](https://pypi.org/project/jax-md) (๐ฅ 23K / month ยท ๐ฆ 25 ยท โฑ๏ธ 10.06.2026):
TorchSim (๐ฅ25 ยท โญ 500) - Torch-native, batchable, atomistic simulations. MIT HTC UIP ML-IAP structure-optimization
- [GitHub](https://github.com/TorchSim/torch-sim) (๐จโ๐ป 39 ยท ๐ 110 ยท ๐ 180 - 10% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/Radical-AI/torch-sim
- [PyPi](https://pypi.org/project/torch-sim-atomistic) (๐ฅ 23K / month ยท ๐ฆ 37 ยท โฑ๏ธ 23.09.2026):
pip install torch-sim-atomistic
GPUMD (๐ฅ23 ยท โญ 860) - 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) (๐จโ๐ป 70 ยท ๐ 210 ยท ๐ 290 - 5% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/brucefan1983/GPUMD
mlcolvar (๐ฅ20 ยท โญ 150) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling
- [GitHub](https://github.com/luigibonati/mlcolvar) (๐จโ๐ป 15 ยท ๐ 35 ยท ๐ฆ 13 ยท ๐ 120 - 7% open ยท โฑ๏ธ 15.09.2026):
git clone https://github.com/luigibonati/mlcolvar
- [PyPi](https://pypi.org/project/mlcolvar) (๐ฅ 290 / month ยท ๐ฆ 4 ยท โฑ๏ธ 03.02.2026):
FitSNAP (๐ฅ18 ยท โญ 190 ยท ๐ค) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0
- [GitHub](https://github.com/FitSNAP/FitSNAP) (๐จโ๐ป 23 ยท ๐ 65 ยท ๐ฅ 19 ยท ๐ 84 - 25% open ยท โฑ๏ธ 17.10.2025):
git clone https://github.com/FitSNAP/FitSNAP
- [Conda](https://anaconda.org/conda-forge/fitsnap3) (๐ฅ 20K ยท โฑ๏ธ 06.09.2026):
conda install -c conda-forge fitsnap3
OpenMM-ML (๐ฅ18 ยท โญ 180) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP
- [GitHub](https://github.com/openmm/openmm-ml) (๐จโ๐ป 12 ยท ๐ 52 ยท ๐ฆ 2 ยท ๐ 85 - 31% open ยท โฑ๏ธ 20.10.2026):
git clone https://github.com/openmm/openmm-ml
- [Conda](https://anaconda.org/conda-forge/openmm-ml) (๐ฅ 99K ยท โฑ๏ธ 16.09.2026):
conda install -c conda-forge openmm-ml
openmm-torch (๐ฅ16 ยท โญ 220) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++
- [GitHub](https://github.com/openmm/openmm-torch) (๐จโ๐ป 9 ยท ๐ 33 ยท ๐ 110 - 33% open ยท โฑ๏ธ 24.06.2026):
git clone https://github.com/openmm/openmm-torch
- [Conda](https://anaconda.org/conda-forge/openmm-torch) (๐ฅ 1.4M ยท โฑ๏ธ 17.06.2026):
conda install -c conda-forge openmm-torch
pair_allegro (๐ฅ13 ยท โญ 65) - 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) (๐จโ๐ป 6 ยท ๐ 12 ยท ๐ 52 - 23% open ยท โฑ๏ธ 14.05.2026):
git clone https://github.com/mir-group/pair_allegro
DMFF (๐ฅ12 ยท โญ 200) - 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) (๐จโ๐ป 17 ยท ๐ 49 ยท ๐ 33 - 39% open ยท โฑ๏ธ 07.04.2026):
git clone https://github.com/deepmodeling/DMFF
Psiflow (๐ฅ12 ยท โญ 140) - scalable molecular simulation. MIT ML-IAP active-learning sampling
- [GitHub](https://github.com/molmod/psiflow) (๐จโ๐ป 5 ยท ๐ 16 ยท ๐ 62 - 20% open ยท โฑ๏ธ 28.04.2026):
git clone https://github.com/molmod/psiflow
PACE (๐ฅ8 ยท โญ 34 ยท ๐ค) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom
- [GitHub](https://github.com/ICAMS/lammps-user-pace) (๐จโ๐ป 8 ยท ๐ 17 ยท ๐ 11 - 45% open ยท โฑ๏ธ 03.12.2025):
git clone https://github.com/ICAMS/lammps-user-pace
MUSE (๐ฅ5 ยท โญ 11) - A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and.. MIT ML-IAP Defects & Disorder
- [GitHub](https://github.com/chiang-yuan/muse) (๐จโ๐ป 2 ยท ๐ฆ 1 ยท ๐ 2 - 50% open ยท โฑ๏ธ 22.04.2026):
git clone https://github.com/chiang-yuan/muse
Show 3 hidden projects...
- pair_nequip (๐ฅ10 ยท โญ 44 ยท ๐) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn
- SOMD (๐ฅ4 ยท โญ 17 ยท ๐ค) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning
- interface-lammps-mlip-3 (๐ฅ3 ยท โญ 5 ยท ๐) - An interface between LAMMPS and MLIP (version 3). GPL-2.0

Projects that focus on probabilistic, Bayesian, Gaussian process and adversarial methods for atomistic ML, for optimization, uncertainty quantification (UQ), etc.
thermo (๐ฅ6 ยท โญ 18) - Data-driven risk-conscious thermoelectric materials discovery. MIT materials-discovery experimental-data active-learning transport-phenomena
- [GitHub](https://github.com/janosh/thermo) (๐ 4 ยท โฑ๏ธ 16.06.2026):
git clone https://github.com/janosh/thermo

Projects that focus on reinforcement learning for atomistic ML.
Show 2 hidden projects...
- ReLeaSE (๐ฅ11 ยท โญ 370 ยท ๐) - Deep Reinforcement Learning for de-novo Drug Design. MIT drug-discovery
- CatGym (๐ฅ5 ยท โญ 13 ยท ๐) - Surface segregation using Deep Reinforcement Learning. GPL

Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering.
cdk (๐ฅ27 ยท โญ 610) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
- [GitHub](https://github.com/cdk/cdk) (๐จโ๐ป 170 ยท ๐ 180 ยท ๐ฅ 150K ยท ๐ 340 - 3% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/cdk/cdk
- [Maven](https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle) (๐ฆ 18 ยท โฑ๏ธ 03.03.2026):
<dependency>
<groupId>org.openscience.cdk</groupId>
<artifactId>cdk-bundle</artifactId>
<version>[VERSION]</version>
</dependency>
ChemML (๐ฅ18 ยท โญ 180) - ChemML is a machine learning and informatics program suite for the chemical and materials sciences. BSD-3 cheminformatics active-learning workflows
- [GitHub](https://github.com/hachmannlab/chemml) (๐จโ๐ป 19 ยท ๐ 33 ยท ๐ฅ 14 ยท ๐ฆ 8 ยท ๐ 16 - 43% open ยท โฑ๏ธ 28.07.2026):
git clone https://github.com/hachmannlab/chemml
- [PyPi](https://pypi.org/project/chemml) (๐ฅ 95 / month ยท ๐ฆ 2 ยท โฑ๏ธ 08.10.2023):
ElementEmbeddings (๐ฅ17 ยท โญ 52) - Python package to interact with high-dimensional representations of the chemical elements. MIT XAI USL viz
- [GitHub](https://github.com/WMD-group/ElementEmbeddings) (๐จโ๐ป 10 ยท ๐ 5 ยท ๐ฆ 6 ยท โฑ๏ธ 04.10.2026):
git clone https://github.com/WMD-group/ElementEmbeddings
- [PyPi](https://pypi.org/project/ElementEmbeddings) (๐ฅ 470 / month ยท ๐ฆ 2 ยท โฑ๏ธ 14.07.2026):
pip install ElementEmbeddings
- [Conda](https://anaconda.org/conda-forge/elementembeddings) (๐ฅ 6K ยท โฑ๏ธ 17.09.2026):
conda install -c conda-forge elementembeddings
Featomic (๐ฅ16 ยท โญ 83 ยท ๐) - Computing representations for atomistic machine learning. BSD-3 Rust C++
- [GitHub](https://github.com/metatensor/featomic) (๐จโ๐ป 21 ยท ๐ 21 ยท ๐ฅ 1.1K ยท ๐ฆ 2 ยท ๐ 90 - 47% open ยท โฑ๏ธ 01.10.2026):
git clone https://github.com/metatensor/featomic
pySIPFENN (๐ฅ15 ยท โญ 24) - 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) (๐จโ๐ป 5 ยท ๐ 6 ยท ๐ฅ 140 ยท ๐ฆ 7 ยท ๐ 10 - 70% open ยท โฑ๏ธ 18.07.2026):
git clone https://github.com/PhasesResearchLab/pySIPFENN
- [PyPi](https://pypi.org/project/pysipfenn) (๐ฅ 140 / month ยท โฑ๏ธ 18.07.2026):
- [Conda](https://anaconda.org/conda-forge/pysipfenn) (๐ฅ 29K ยท โฑ๏ธ 05.09.2026):
conda install -c conda-forge pysipfenn
SISSO (๐ฅ12 ยท โญ 380 ยท ๐ค) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran
- [GitHub](https://github.com/rouyang2017/SISSO) (๐จโ๐ป 3 ยท ๐ 95 ยท ๐ 78 - 23% open ยท โฑ๏ธ 26.01.2026):
git clone https://github.com/rouyang2017/SISSO
GlassPy (๐ฅ12 ยท โญ 43 ยท ๐ค) - Python module for scientists working with glass materials. GPL-3.0
- [GitHub](https://github.com/drcassar/glasspy) (๐จโ๐ป 2 ยท ๐ 9 ยท ๐ฆ 7 ยท ๐ 17 - 47% open ยท โฑ๏ธ 17.03.2026):
git clone https://github.com/drcassar/glasspy
- [PyPi](https://pypi.org/project/glasspy) (๐ฅ 330 / month ยท โฑ๏ธ 13.03.2026):
BenchML (๐ฅ11 ยท โญ 15 ยท ๐ค) - ML benchmarking and pipeling framework. Apache-2 benchmarking
- [GitHub](https://github.com/capoe/benchml) (๐จโ๐ป 9 ยท ๐ 6 ยท ๐ 13 - 23% open ยท โฑ๏ธ 28.10.2025):
git clone https://github.com/capoe/benchml
- [PyPi](https://pypi.org/project/benchml) (๐ฅ 22 / month ยท โฑ๏ธ 14.07.2022):
PDynA (๐ฅ10 ยท โญ 57) - Python package to analyse the structural dynamics of perovskites. MIT MD
- [GitHub](https://github.com/WMD-group/PDynA) (๐จโ๐ป 4 ยท ๐ 6 ยท ๐ฆ 2 ยท ๐ 6 - 16% open ยท โฑ๏ธ 15.08.2026):
git clone https://github.com/WMD-group/PDynA
- [PyPi](https://pypi.org/project/pdyna) (๐ฅ 19 / month ยท โฑ๏ธ 23.09.2024):
ElemNet (๐ฅ7 ยท โญ 100 ยท ๐ค) - Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction. Unlicensed single-paper
- [GitHub](https://github.com/NU-CUCIS/ElemNet) (๐จโ๐ป 4 ยท ๐ 35 ยท ๐ 8 - 50% open ยท โฑ๏ธ 13.01.2026):
git clone https://github.com/NU-CUCIS/ElemNet
MOLPIPx (๐ฅ7 ยท โญ 50 ยท ๐ค) - Differentiable version of Permutationally Invariant Polynomial (PIP) models in JAX and Rust. Apache-2 Python Rust
- [GitHub](https://github.com/ChemAI-Lab/molpipx) (๐จโ๐ป 13 ยท ๐ 1 ยท โฑ๏ธ 27.03.2026):
git clone https://github.com/ChemAI-Lab/molpipx
soap_turbo (๐ฅ6 ยท โญ 9) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
- [GitHub](https://github.com/libAtoms/soap_turbo) (๐จโ๐ป 4 ยท ๐ 10 ยท ๐ 8 - 62% open ยท โฑ๏ธ 29.05.2026):
git clone https://github.com/libAtoms/soap_turbo
Show 19 hidden projects...
- DScribe (๐ฅ25 ยท โญ 470 ยท ๐) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2
- CatLearn (๐ฅ16 ยท โญ 120 ยท ๐) - GPL-3.0 surface-science
- MODNet (๐ฅ15 ยท โญ 110 ยท ๐) - MODNet: a framework for machine learning materials properties. MIT pretrained small-data transfer-learning
- Librascal (๐ฅ12 ยท โญ 83 ยท ๐) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1
- CBFV (๐ฅ12 ยท โญ 34 ยท ๐) - Tool to quickly create a composition-based feature vector. Unlicensed
- SkipAtom (๐ฅ10 ยท โญ 28 ยท ๐) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
- cmlkit (๐ฅ9 ยท โญ 33 ยท ๐) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking
- NICE (๐ฅ7 ยท โญ 12 ยท ๐) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT
- SISSO++ (๐ฅ7 ยท โญ 6 ยท ๐) - C++ Implementation of SISSO with python bindings. Apache-2 C++
- milad (๐ฅ6 ยท โญ 35 ยท ๐) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative
- SA-GPR (๐ฅ6 ยท โญ 23 ยท ๐) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang
- fplib (๐ฅ6 ยท โญ 7 ยท ๐) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper
- SOAPxx (๐ฅ6 ยท โญ 7 ยท ๐) - A SOAP implementation. GPL-2.0 C++
- pyLODE (๐ฅ6 ยท โญ 3 ยท ๐) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
- AMP (๐ฅ6 ยท ๐) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed
- MXenes4HER (๐ฅ5 ยท โญ 7 ยท ๐) - 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
- automl-materials (๐ฅ4 ยท โญ 5 ยท ๐) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper
- ML-for-CurieTemp-Predictions (๐ฅ4 ยท โญ 4 ยท ๐) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
- magnetism-prediction (๐ฅ4 ยท โญ 2) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper

General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN).
PyG Models (๐ฅ34 ยท โญ 24K ยท ๐) - Representation learning models implemented in PyTorch Geometric. MIT general-ml
- [GitHub](https://github.com/pyg-team/pytorch_geometric) (๐จโ๐ป 570 ยท ๐ 4.1K ยท ๐ฆ 12K ยท ๐ 4.2K - 33% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/pyg-team/pytorch_geometric
e3nn (๐ฅ28 ยท โญ 1.3K ยท ๐ค) - A modular framework for neural networks with Euclidean symmetry. MIT
- [GitHub](https://github.com/e3nn/e3nn) (๐จโ๐ป 38 ยท ๐ 180 ยท ๐ฆ 720 ยท ๐ 180 - 17% open ยท โฑ๏ธ 13.02.2026):
git clone https://github.com/e3nn/e3nn
- [PyPi](https://pypi.org/project/e3nn) (๐ฅ 300K / month ยท ๐ฆ 74 ยท โฑ๏ธ 13.02.2026):
- [Conda](https://anaconda.org/conda-forge/e3nn) (๐ฅ 92K ยท โฑ๏ธ 29.04.2026):
conda install -c conda-forge e3nn
MatGL (Materials Graph Library) (๐ฅ27 ยท โญ 580) - Graph deep learning library for materials. BSD-3 ML-IAP pretrained multifidelity
- [GitHub](https://github.com/materialyzeai/matgl) (๐จโ๐ป 30 ยท ๐ 120 ยท ๐ฆ 100 ยท ๐ 160 - 2% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/materialsvirtuallab/matgl
- [PyPi](https://pypi.org/project/matgl) (๐ฅ 29K / month ยท ๐ฆ 46 ยท โฑ๏ธ 21.10.2026):
- [Docker Hub](https://hub.docker.com/r/materialsvirtuallab/matgl) (๐ฅ 710 ยท โญ 1 ยท โฑ๏ธ 26.04.2026):
docker pull materialsvirtuallab/matgl
SchNetPack (๐ฅ24 ยท โญ 940) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT
- [GitHub](https://github.com/atomistic-machine-learning/schnetpack) (๐จโ๐ป 43 ยท ๐ 250 ยท ๐ฆ 110 ยท ๐ 280 - 1% open ยท โฑ๏ธ 29.09.2026):
git clone https://github.com/atomistic-machine-learning/schnetpack
- [PyPi](https://pypi.org/project/schnetpack) (๐ฅ 760 / month ยท ๐ฆ 4 ยท โฑ๏ธ 19.12.2025):
e3nn-jax (๐ฅ22 ยท โญ 240) - jax library for E3 Equivariant Neural Networks. Apache-2
- [GitHub](https://github.com/e3nn/e3nn-jax) (๐จโ๐ป 8 ยท ๐ 23 ยท ๐ฆ 100 ยท ๐ 26 - 15% open ยท โฑ๏ธ 01.04.2026):
git clone https://github.com/e3nn/e3nn-jax
- [PyPi](https://pypi.org/project/e3nn-jax) (๐ฅ 80K / month ยท ๐ฆ 35 ยท โฑ๏ธ 01.04.2026):
HydraGNN (๐ฅ17 ยท โญ 130) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3
- [GitHub](https://github.com/ORNL/HydraGNN) (๐จโ๐ป 22 ยท ๐ 46 ยท ๐ฆ 3 ยท ๐ 65 - 36% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/ORNL/HydraGNN
Graphormer (๐ฅ16 ยท โญ 2.5K) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT transformer pretrained
- [GitHub](https://github.com/microsoft/Graphormer) (๐จโ๐ป 18 ยท ๐ 360 ยท ๐ 160 - 58% open ยท โฑ๏ธ 24.09.2026):
git clone https://github.com/microsoft/Graphormer
hippynn (๐ฅ13 ยท โญ 98) - python library for atomistic machine learning. Custom workflows
- [GitHub](https://github.com/lanl/hippynn) (๐จโ๐ป 22 ยท ๐ 37 ยท ๐ฆ 4 ยท ๐ 42 - 33% open ยท โฑ๏ธ 22.08.2026):
git clone https://github.com/lanl/hippynn
Neural fingerprint (nfp) (๐ฅ13 ยท โญ 62) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom
- [GitHub](https://github.com/NatLabRockies/nfp) (๐จโ๐ป 6 ยท ๐ 35 ยท ๐ฆ 17 ยท ๐ 8 - 25% open ยท โฑ๏ธ 02.09.2026):
git clone https://github.com/NREL/nfp
matsciml (๐ฅ11) - 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) (๐จโ๐ป 14 ยท ๐ 67 - 35% open ยท โฑ๏ธ 06.10.2026):
git clone https://github.com/IntelLabs/matsciml
SE(3)-Transformers (๐ฅ9 ยท โญ 580) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer
- [GitHub](https://github.com/FabianFuchsML/se3-transformer-public) (๐จโ๐ป 2 ยท ๐ 77 ยท ๐ 29 - 37% open ยท โฑ๏ธ 03.04.2026):
git clone https://github.com/FabianFuchsML/se3-transformer-public
molecularGNN_smiles (๐ฅ8 ยท โญ 340) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
- [GitHub](https://github.com/masashitsubaki/molecularGNN_smiles) (๐จโ๐ป 1 ยท ๐ 80 ยท ๐ 7 - 85% open ยท โฑ๏ธ 25.06.2026):
git clone https://github.com/masashitsubaki/molecularGNN_smiles
Crystalframer (๐ฅ8 ยท โญ 21 ยท ๐ค) - 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) (๐จโ๐ป 3 ยท ๐ 3 ยท ๐ฅ 40 ยท โฑ๏ธ 16.10.2025):
git clone https://github.com/omron-sinicx/crystalframer
DeeperGATGNN (๐ฅ7 ยท โญ 66 ยท ๐ค) - Scalable graph neural networks for materials property prediction. MIT
- [GitHub](https://github.com/usccolumbia/deeperGATGNN) (๐จโ๐ป 3 ยท ๐ 8 ยท โฑ๏ธ 02.02.2026):
git clone https://github.com/usccolumbia/deeperGATGNN
Show 49 hidden projects...
- Deep Graph Library (DGL) (๐ฅ36 ยท โญ 14K ยท ๐) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2
- dgl-lifesci (๐ฅ24 ยท โญ 810 ยท ๐) - Python package for graph neural networks in chemistry and biology. Apache-2
- NVIDIA Deep Learning Examples for Tensor Cores (๐ฅ20 ยท โญ 15K ยท ๐) - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and.. Custom educational drug-discovery
- ALIGNN (๐ฅ20 ยท โญ 330 ยท ๐) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ.. Custom
- DIG: Dive into Graphs (๐ฅ19 ยท โญ 2K ยท ๐) - A library for graph deep learning research. GPL-3.0
- Uni-Mol (๐ฅ18 ยท โญ 1.2K ยท ๐) - Official Repository for the Uni-Mol Series Methods. MIT pretrained
- escnn (๐ฅ16 ยท โญ 550 ยท ๐) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
- kgcnn (๐ฅ16 ยท โญ 120 ยท ๐) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT
- benchmarking-gnns (๐ฅ14 ยท โญ 2.7K ยท ๐) - Repository for benchmarking graph neural networks (JMLR 2023). MIT single-paper benchmarking
- Crystal Graph Convolutional Neural Networks (CGCNN) (๐ฅ13 ยท โญ 900 ยท ๐) - Crystal graph convolutional neural networks for predicting material properties. MIT
- xtal2png (๐ฅ13 ยท โญ 39 ยท ๐) - Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning.. MIT computer-vision
- Compositionally-Restricted Attention-Based Network (CrabNet) (๐ฅ13 ยท โญ 18 ยท ๐) - Predict materials properties using only the composition information!. MIT
- FAENet (๐ฅ11 ยท โญ 35 ยท ๐) - Frame Averaging Equivariant GNN for materials modeling. MIT
- pretrained-gnns (๐ฅ10 ยท โญ 1.1K ยท ๐) - Strategies for Pre-training Graph Neural Networks. MIT pretrained
- GDC (๐ฅ10 ยท โญ 280 ยท ๐) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative
- Atom2Vec (๐ฅ10 ยท โญ 38 ยท ๐) - Atom2Vec: a simple way to describe atoms for machine learning. MIT
- GATGNN: Global Attention Graph Neural Network (๐ฅ9 ยท โญ 88 ยท ๐) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT
- T-e3nn (๐ฅ9 ยท โญ 21 ยท ๐) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
- ai4material_design (๐ฅ9 ยท โญ 8 ยท ๐) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pretrained material-defect
- Equiformer (๐ฅ8 ยท โญ 290 ยท ๐) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer
- graphite (๐ฅ8 ยท โญ 110 ยท ๐) - A repository for implementing graph network models based on atomic structures. MIT
- Graph-Aware-Transformers (๐ฅ8 ยท โญ 70 ยท ๐) - Graph-Aware Attention for Adaptive Dynamics in Transformers. Apache-2 transformer graph-data pretrained single-paper
- UVVisML (๐ฅ8 ยท โญ 37 ยท ๐) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
- GNNOpt (๐ฅ8 ยท โญ 34 ยท ๐) - Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures. MIT optical-properties single-paper
- tensorfieldnetworks (๐ฅ7 ยท โญ 160 ยท ๐) - Rotation- and translation-equivariant neural networks for 3D point clouds. MIT
- Geom3D (๐ฅ7 ยท โญ 130 ยท ๐) - Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023. MIT benchmarking single-paper
- DTNN (๐ฅ7 ยท โญ 79 ยท ๐) - Deep Tensor Neural Network. MIT
- Cormorant (๐ฅ7 ยท โญ 61 ยท ๐) - Codebase for Cormorant Neural Networks. Custom
- escnn_jax (๐ฅ7 ยท โญ 33 ยท ๐) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
- CGAT (๐ฅ7 ยท โญ 31 ยท ๐) - Crystal graph attention neural networks for materials prediction. MIT
- MACE-Layer (๐ฅ6 ยท โญ 50 ยท ๐) - Higher order equivariant graph neural networks for 3D point clouds. MIT
- AdsorbML (๐ฅ6 ยท โญ 46 ยท ๐) - MIT surface-science single-paper
- charge_transfer_nnp (๐ฅ6 ยท โญ 37 ยท ๐) - Graph neural network potential with charge transfer. MIT electrostatics
- Crystalformer (๐ฅ6 ยท โญ 29 ยท ๐) - The official code respository for Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding (ICLR.. MIT transformer single-paper
- GLAMOUR (๐ฅ6 ยท โญ 27 ยท ๐) - Graph Learning over Macromolecule Representations. MIT single-paper
- FieldSchNet (๐ฅ6 ยท โญ 25 ยท ๐) - Deep neural network for molecules in external fields. MIT
- ML4pXRDs (๐ฅ6 ยท โญ 3 ยท ๐) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper
- Autobahn (๐ฅ5 ยท โญ 30 ยท ๐) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
- CraTENet (๐ฅ5 ยท โญ 18 ยท ๐) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
- SCFNN (๐ฅ5 ยท โญ 15 ยท ๐) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
- gkx: Green-Kubo Method in JAX (๐ฅ5 ยท โญ 9 ยท ๐) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena
- Per-site PAiNN (๐ฅ5 ยท โญ 2 ยท ๐) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pretrained single-paper
- Per-Site CGCNN (๐ฅ5 ยท โญ 1 ยท ๐) - Crystal graph convolutional neural networks for predicting material properties. MIT pretrained single-paper
- Graph Transport Network (๐ฅ4 ยท โญ 15 ยท ๐) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena
- atom_by_atom (๐ฅ4 ยท โญ 13 ยท ๐) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper
- EGraFFBench (๐ฅ4 ยท โญ 12 ยท ๐) - Unlicensed single-paper benchmarking ML-IAP
- Element encoder (๐ฅ3 ยท โญ 6 ยท ๐) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
- Point Edge Transformer (๐ฅ2) - Smooth, exact rotational symmetrization for deep learning on point clouds. CC-BY-4.0
- SphericalNet (๐ฅ1 ยท โญ 3 ยท ๐) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in.. Unlicensed

Machine-learned interatomic potentials (ML-IAP) that have been trained on large, chemically and structural diverse datasets. For materials, this means e.g. datasets that include a majority of the periodic table.
๐ TeaNet - Universal neural network interatomic potential inspired by iterative electronic relaxations.. ML-IAP
๐ PreFerred Potential (PFP) - Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9. ML-IAP proprietary
FAIRChem EquiformerV2 models (๐ฅ31 ยท โญ 2.3K) - FAIRChem implementation of Equiformer V2 (eqV2) models. MIT pretrained UIP rep-learn catalysis
- [GitHub](https://github.com/facebookresearch/fairchem) (๐จโ๐ป 75 ยท ๐ 520 ยท ๐ 620 - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/FAIR-Chem/fairchem
- [PyPi](https://pypi.org/project/fairchem-core) (๐ฅ 19K / month ยท ๐ฆ 95 ยท โฑ๏ธ 22.10.2026):
pip install fairchem-core
FAIRChem eSEN models (๐ฅ31 ยท โญ 2.3K) - FAIRChem implementation of Smooth Energy Network (eSEN) models arXiv:2502.12147. MIT pretrained UIP rep-learn catalysis
- [GitHub](https://github.com/facebookresearch/fairchem) (๐จโ๐ป 75 ยท ๐ 520 ยท ๐ 620 - 2% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/FAIR-Chem/fairchem
- [PyPi](https://pypi.org/project/fairchem-core) (๐ฅ 19K / month ยท ๐ฆ 95 ยท โฑ๏ธ 22.10.2026):
pip install fairchem-core
DPA-2 (๐ฅ31 ยท โญ 2.1K) - A large atomic model as a multi-task learner https://arxiv.org/abs/2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets
- [GitHub](https://github.com/deepmodeling/deepmd-kit) (๐จโ๐ป 96 ยท ๐ 640 ยท ๐ฅ 79K ยท ๐ฆ 63 ยท ๐ 1.3K - 15% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/deepmodeling/deepmd-kit
- [PyPi](https://pypi.org/project/deepmd-kit) (๐ฅ 17K / month ยท ๐ฆ 22 ยท โฑ๏ธ 19.08.2026):
- [Conda](https://anaconda.org/conda-forge/deepmd-kit) (๐ฅ 3.4M ยท โฑ๏ธ 01.09.2026):
conda install -c conda-forge deepmd-kit
- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (๐ฅ 7.7K ยท โญ 1 ยท โฑ๏ธ 22.08.2026):
docker pull deepmodeling/deepmd-kit
DeePMD-DPA3 (๐ฅ31 ยท โญ 2.1K) - Successor of DPA-2. LGPL-3.0 ML-IAP pretrained workflows datasets
- [GitHub](https://github.com/deepmodeling/deepmd-kit) (๐จโ๐ป 96 ยท ๐ 640 ยท ๐ฅ 79K ยท ๐ฆ 63 ยท ๐ 1.3K - 15% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/deepmodeling/deepmd-kit
- [PyPi](https://pypi.org/project/deepmd-kit) (๐ฅ 17K / month ยท ๐ฆ 22 ยท โฑ๏ธ 19.08.2026):
- [Conda](https://anaconda.org/conda-forge/deepmd-kit) (๐ฅ 3.4M ยท โฑ๏ธ 01.09.2026):
conda install -c conda-forge deepmd-kit
- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (๐ฅ 7.7K ยท โญ 1 ยท โฑ๏ธ 22.08.2026):
docker pull deepmodeling/deepmd-kit
MACE-FOUNDATION models (๐ฅ24 ยท โญ 1.2K) - MACE foundation models (MP, OMAT, mh-1). MIT ML-IAP pretrained rep-learn MD
- [GitHub](https://github.com/ACEsuit/mace-foundations) (๐จโ๐ป 6 ยท ๐ 420 ยท ๐ฅ 830K ยท ๐ 45 - 22% open ยท โฑ๏ธ 26.08.2026):
git clone https://github.com/ACEsuit/mace-foundations
- [PyPi](https://pypi.org/project/mace-torch) (๐ฅ 76K / month ยท ๐ฆ 110 ยท โฑ๏ธ 10.05.2026):
MatterSim (๐ฅ24 ยท โญ 610) - 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) (๐จโ๐ป 27 ยท ๐ 97 ยท ๐ฅ 46 ยท ๐ 48 - 10% open ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/microsoft/mattersim
- [PyPi](https://pypi.org/project/mattersim) (๐ฅ 24K / month ยท ๐ฆ 29 ยท โฑ๏ธ 28.05.2026):
SevenNet (๐ฅ22 ยท โญ 280 ยท ๐) - 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) (๐จโ๐ป 22 ยท ๐ 57 ยท ๐ฅ 46K ยท ๐ 99 - 18% open ยท โฑ๏ธ 06.10.2026):
git clone https://github.com/MDIL-SNU/SevenNet
- [PyPi](https://pypi.org/project/sevenn) (๐ฅ 16K / month ยท ๐ฆ 28 ยท โฑ๏ธ 19.06.2026):
Orb Models (๐ฅ21 ยท โญ 620) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained
- [GitHub](https://github.com/orbital-materials/orb-models) (๐จโ๐ป 17 ยท ๐ 89 ยท ๐ฆ 49 ยท ๐ 82 - 10% open ยท โฑ๏ธ 26.09.2026):
git clone https://github.com/orbital-materials/orb-models
- [PyPi](https://pypi.org/project/orb-models) (๐ฅ 13K / month ยท ๐ฆ 38 ยท โฑ๏ธ 26.05.2026):
CHGNet (๐ฅ20 ยท โญ 410) - 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) (๐จโ๐ป 14 ยท ๐ 100 ยท ๐ฆ 83 ยท ๐ 78 - 5% open ยท โฑ๏ธ 15.09.2026):
git clone https://github.com/CederGroupHub/chgnet
- [PyPi](https://pypi.org/project/chgnet) (๐ฅ 26K / month ยท ๐ฆ 33 ยท โฑ๏ธ 22.09.2025):
PET-MAD (๐ฅ15 ยท โญ 240 ยท ๐ฃ) - Archive of the pet-mad repository. BSD-3 ML-IAP MD rep-learn transformer
- [GitHub](https://github.com/lab-cosmo/pet-mad) (๐จโ๐ป 12 ยท ๐ 24 ยท โฑ๏ธ 25.09.2026):
git clone https://github.com/lab-cosmo/pet-mad
- [PyPi](https://pypi.org/project/pet-mad) (๐ฅ 110 / month ยท ๐ฆ 12 ยท โฑ๏ธ 12.12.2025):
- [Conda](https://anaconda.org/conda-forge/pet-mad):
conda install -c conda-forge pet-mad
GRACE (๐ฅ12 ยท โญ 110) - 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) (๐จโ๐ป 5 ยท ๐ 20 ยท ๐ฆ 17 ยท ๐ 22 - 68% open ยท โฑ๏ธ 17.10.2026):
git clone https://github.com/ICAMS/grace-tensorpotential
MLIP Arena Leaderboard (๐ฅ11 ยท โญ 110) - [NeurIPS 25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic.. Apache-2 ML-IAP benchmarking
- [GitHub](https://github.com/atomind-ai/mlip-arena) (๐จโ๐ป 3 ยท ๐ 10 ยท ๐ฆ 2 ยท ๐ 23 - 56% open ยท โฑ๏ธ 16.06.2026):
git clone https://github.com/atomind-ai/mlip-arena
Show 5 hidden projects...
- M3GNet (๐ฅ16 ยท โญ 330 ยท ๐) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained
- EScAIP (๐ฅ7 ยท โญ 60 ยท ๐) - [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential. MIT ML-IAP rep-learn transformer single-paper
- ffonons (๐ฅ6 ยท โญ 24 ยท ๐) - Phonons from ML force fields. MIT benchmarking density-of-states
- CHIPS-FF (๐ฅ6 ยท โญ 14 ยท ๐) - Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093. Custom benchmarking structure-optimization MD materials-discovery transport-phenomena
- Joint Multidomain Pre-Training (JMP) (๐ฅ5 ยท โญ 62 ยท ๐) - 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

Projects that focus on unsupervised, semi- or self-supervised learning for atomistic ML, such as dimensionality reduction, clustering, contrastive learning, etc.
DADApy (๐ฅ23 ยท โญ 160) - Distance-based Analysis of DAta-manifolds in python. Apache-2
- [GitHub](https://github.com/sissa-data-science/DADApy) (๐จโ๐ป 24 ยท ๐ 25 ยท ๐ 51 - 31% open ยท โฑ๏ธ 09.10.2026):
git clone https://github.com/sissa-data-science/DADApy
- [PyPi](https://pypi.org/project/dadapy) (๐ฅ 370 / month ยท ๐ฆ 3 ยท โฑ๏ธ 01.05.2026):
Show 9 hidden projects...
- mat_discover (๐ฅ13 ยท โญ 47 ยท ๐) - A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces. MIT materials-discovery rep-eng HTC
- ASAP (๐ฅ12 ยท โญ 160 ยท ๐) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT
- pumml (๐ฅ10 ยท โญ 36 ยท ๐) - Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to.. MIT materials-discovery
- Sketchmap (๐ฅ8 ยท โญ 48 ยท ๐) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++
- 3D-EMGP (๐ฅ5 ยท โญ 33 ยท ๐) - [AAAI 2023] The implementation for the paper Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs. MIT pretrained rep-learn single-paper
- paper-ml-robustness-material-property (๐ฅ5 ยท โญ 4 ยท ๐) - A critical examination of robustness and generalizability of machine learning prediction of materials properties. BSD-3 datasets single-paper
- Coarse-Graining-Auto-encoders (๐ฅ4 ยท โญ 21 ยท ๐) - Implementation of coarse-graining Autoencoders. Unlicensed single-paper
- KmdPlus (๐ฅ4 ยท โญ 8 ยท ๐) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. MIT
- Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( โญ 2) - Provides a workflow to obtain clustering of local environments in dataset of structures. Unlicensed

Projects that focus on visualization (viz.) for atomistic ML.
Crystal Toolkit (๐ฅ26 ยท โญ 200) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT
- [GitHub](https://github.com/materialsproject/crystaltoolkit) (๐จโ๐ป 40 ยท ๐ 66 ยท ๐ฆ 43 ยท ๐ 150 - 45% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/materialsproject/crystaltoolkit
- [PyPi](https://pypi.org/project/crystal-toolkit) (๐ฅ 19K / month ยท ๐ฆ 13 ยท โฑ๏ธ 28.08.2026):
pip install crystal-toolkit
pymatviz (๐ฅ24 ยท โญ 340) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
- [GitHub](https://github.com/janosh/pymatviz) (๐จโ๐ป 15 ยท ๐ 43 ยท ๐ฅ 26K ยท ๐ฆ 37 ยท โฑ๏ธ 19.10.2026):
git clone https://github.com/janosh/pymatviz
- [PyPi](https://pypi.org/project/pymatviz) (๐ฅ 8.6K / month ยท ๐ฆ 10 ยท โฑ๏ธ 07.06.2026):
Elementari (๐ฅ23 ยท โญ 360 ยท ๐) - Interactive browser visualizations for materials science: crystal structures/molecules, trajectories, convex hulls,.. MIT JavaScript
- [GitHub](https://github.com/janosh/matterviz) (๐จโ๐ป 12 ยท ๐ 49 ยท ๐ฅ 18K ยท ๐ฆ 9 ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/janosh/elementari
- [npm](https://www.npmjs.com/package/elementari) (๐ฆ 2 ยท โฑ๏ธ 19.06.2025):
Chemiscope (๐ฅ23 ยท โญ 180) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript
- [GitHub](https://github.com/lab-cosmo/chemiscope) (๐จโ๐ป 27 ยท ๐ 44 ยท ๐ฅ 760 ยท ๐ฆ 6 ยท ๐ 180 - 10% open ยท โฑ๏ธ 22.10.2026):
git clone https://github.com/lab-cosmo/chemiscope
- [npm](https://www.npmjs.com/package/chemiscope) (๐ฅ 300 / month ยท ๐ฆ 5 ยท โฑ๏ธ 13.10.2026):
ZnDraw (๐ฅ18 ยท โญ 51) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript
- [GitHub](https://github.com/zincware/ZnDraw) (๐จโ๐ป 17 ยท ๐ 5 ยท ๐ฆ 16 ยท ๐ 400 - 23% open ยท โฑ๏ธ 27.05.2026):
git clone https://github.com/zincware/ZnDraw
- [PyPi](https://pypi.org/project/zndraw) (๐ฅ 2.9K / month ยท ๐ฆ 5 ยท โฑ๏ธ 01.04.2026):
Show 1 hidden projects...
- Atomvision (๐ฅ11 ยท โญ 37 ยท ๐) - Deep learning framework for atomistic image data. Custom computer-vision experimental-data rep-learn

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 (๐ฅ20 ยท โญ 420) - Deep learning quantum Monte Carlo for electrons in real space. MIT
- [GitHub](https://github.com/deepqmc/deepqmc) (๐จโ๐ป 17 ยท ๐ 63 ยท ๐ฅ 7 ยท ๐ฆ 3 ยท ๐ 65 - 1% open ยท โฑ๏ธ 28.09.2026):
git clone https://github.com/deepqmc/deepqmc
- [PyPi](https://pypi.org/project/deepqmc) (๐ฅ 200 / month ยท โฑ๏ธ 20.07.2026):
FermiNet (๐ฅ17 ยท โญ 860) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer
- [GitHub](https://github.com/google-deepmind/ferminet) (๐จโ๐ป 29 ยท ๐ 170 ยท ๐ 74 - 5% open ยท โฑ๏ธ 15.10.2026):
git clone https://github.com/google-deepmind/ferminet
JaQMC (๐ฅ11 ยท โญ 130 ยท ๐) - JAX accelerated Quantum Monte Carlo. Apache-2
- [GitHub](https://github.com/bytedance/jaqmc) (๐จโ๐ป 11 ยท ๐ 30 ยท ๐ฆ 1 ยท โฑ๏ธ 21.10.2026):
git clone https://github.com/bytedance/jaqmc
Show 4 hidden projects...
- DeepErwin (๐ฅ7 ยท โญ 67 ยท ๐) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom
- ACEpsi.jl (๐ฅ7 ยท โญ 3 ยท ๐) - ACE wave function parameterizations. MIT rep-eng Julia
- LapNet (๐ฅ5 ยท โญ 73 ยท ๐) - Efficient and Accurate Neural-Network Ansatz for Quantum Monte Carlo. Apache-2
- SchNOrb (๐ฅ5 ยท โญ 72 ยท ๐) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT

Contributions are encouraged and always welcome! If you like to add or update projects, choose one of the following ways:
- Open an issue by selecting one of the provided categories from the issue page and fill in the requested information.
- Modify the projects.yaml with your additions or changes, and submit a pull request. This can also be done directly via the Github UI.
If you like to contribute to or share suggestions regarding the project metadata collection or markdown generation, please refer to the best-of-generator repository. If you like to create your own best-of list, we recommend to follow this guide.
For more information on how to add or update projects, please read the contribution guidelines. By participating in this project, you agree to abide by its Code of Conduct.

Credit by: @github.com/JuDFTteam/best-of-atomistic-machine-learning