Best of Atomistic Machine Learning ⚛️🧬💎

🏆  A ranked list of awesome atomistic machine learning (AML) projects. Updated regularly.

This curated list contains 430 awesome open-source projects with a total of 190K stars grouped into 22 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!

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Contents

• Active learning 6 projects
• Community resources 31 projects
• Datasets 46 projects
• Data Structures 4 projects
• Density functional theory (ML-DFT) 33 projects
• Educational Resources 28 projects
• Explainable Artificial intelligence (XAI) 3 projects
• Electronic structure methods (ML-ESM) 5 projects
• General Tools 22 projects
• Generative Models 14 projects
• Interatomic Potentials (ML-IAP) 70 projects
• Language Models 22 projects
• Materials Discovery 12 projects
• Mathematical tools 11 projects
• Molecular Dynamics 10 projects
• Reinforcement Learning 2 projects
• Representation Engineering 25 projects
• Representation Learning 58 projects
• Universal Potentials 12 projects
• Unsupervised Learning 7 projects
• Visualization 6 projects
• Wavefunction methods (ML-WFT) 5 projects
• Others 1 projects

Explanation

• 🥇🥈🥉  Combined project-quality score
• ⭐️  Star count from GitHub
• 🐣  New project (less than 6 months old)
• 💤  Inactive project (6 months no activity)
• 💀  Dead project (12 months no activity)
• 📈📉  Project is trending up or down
• ➕  Project was recently added
• 👨‍💻  Contributors count from GitHub
• 🔀  Fork count from GitHub
• 📋  Issue count from GitHub
• ⏱️  Last update timestamp on package manager
• 📥  Download count from package manager
• 📦  Number of dependent projects

Active learning

Projects that focus on enabling active learning, iterative learning schemes for atomistic ML.

Community resources

Projects that collect atomistic ML resources or foster communication within community.

🔗 AI for Science Map - Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..

🔗 Atomic Cluster Expansion - Atomic Cluster Expansion (ACE) community homepage.

🔗 CrystaLLM - Generate a crystal structure from a composition. language-models generative pretrained transformer

🔗 GAP-ML.org community homepage ML-IAP

🔗 matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..

🔗 Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.

🔗 ACE / GRACE support - Support forum for the Atomic Cluster Expansion (ACE) and extensions.

Datasets

Datasets, databases and trained models for atomistic ML.

🔗 Alexandria Materials Database - A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..

🔗 Catalysis Hub - A web-platform for sharing data and software for computational catalysis research!.

🔗 Citrination Datasets - AI-Powered Materials Data Platform. Open Citrination has been decommissioned.

🔗 crystals.ai - Curated datasets for reproducible AI in materials science.

🔗 DeepChem Models - DeepChem models on HuggingFace. model-repository pretrained language-models

🔗 Graphs of Materials Project 20190401 - The dataset used to train the MEGNet interatomic potential. ML-IAP

🔗 HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP

🔗 JARVIS-Leaderboard ( ⭐ 61) - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational

🔗 Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.

🔗 Materials Project Trajectory (MPtrj) Dataset - The dataset used to train the CHGNet universal potential. UIP

🔗 matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.

🔗 MPF.2021.2.8 - The dataset used to train the M3GNet universal potential. UIP

🔗 NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..

🔗 Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.

🔗 sGDML Datasets - MD17, MD22, DFT datasets.

🔗 MoleculeNet - A Benchmark for Molecular Machine Learning. benchmarking

🔗 ZINC15 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules

🔗 ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules

Data Structures

Projects that focus on providing data structures used in atomistic machine learning.

Density functional theory (ML-DFT)

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.

🔗 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

Educational Resources

Tutorials, guides, cookbooks, recipes, etc.

🔗 AI for Science 101 community-resource rep-learn

🔗 AL4MS 2023 workshop tutorials active-learning

🔗 Quantum Chemistry in the Age of Machine Learning - Book, 2022.

Explainable Artificial intelligence (XAI)

Projects that focus on explainability and model interpretability in atomistic ML.

Electronic structure methods (ML-ESM)

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.

General Tools

General tools for atomistic machine learning.

Generative Models

Projects that implement generative models for atomistic ML.

Interatomic Potentials (ML-IAP)

Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics.

Language Models

Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML.

Materials Discovery

Projects that implement materials discovery methods using atomistic ML.

🔗 MatterGen - A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687. generative proprietary

Mathematical tools

Projects that implement mathematical objects used in atomistic machine learning.

Molecular Dynamics

Projects that simplify the integration of molecular dynamics and atomistic machine learning.

Reinforcement Learning

Projects that focus on reinforcement learning for atomistic ML.

Representation Engineering

Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering.

Representation Learning

General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN).

Universal Potentials

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.

🔗 TeaNet - Universal neural network interatomic potential inspired by iterative electronic relaxations.. ML-IAP

🔗 PreFerred Potential (PFP) - Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9. ML-IAP proprietary

🔗 MatterSim - A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967. ML-IAP active-learning proprietary

Unsupervised Learning

Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization.

Visualization

Projects that focus on visualization (viz.) for atomistic ML.

Wavefunction methods (ML-WFT)

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.

Others

Contribution

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.

License