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https://github.com/JuDFTteam/best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
https://github.com/JuDFTteam/best-of-atomistic-machine-learning
List: best-of-atomistic-machine-learning
ai4science atomistic-machine-learning awesome-list best-of-list computational-chemistry computational-materials-science condensed-matter density-functional-theory drug-discovery electronic-structure interatomic-potentials materials-discovery materials-informatics molecular-dynamics quantum-chemistry scientific-computing scientific-machine-learning surrogate-models
Last synced: 3 months ago
JSON representation
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
- Host: GitHub
- URL: https://github.com/JuDFTteam/best-of-atomistic-machine-learning
- Owner: JuDFTteam
- License: cc-by-sa-4.0
- Created: 2023-06-09T20:42:35.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-08T18:16:44.000Z (3 months ago)
- Last Synced: 2024-08-08T23:18:23.421Z (3 months ago)
- Topics: ai4science, atomistic-machine-learning, awesome-list, best-of-list, computational-chemistry, computational-materials-science, condensed-matter, density-functional-theory, drug-discovery, electronic-structure, interatomic-potentials, materials-discovery, materials-informatics, molecular-dynamics, quantum-chemistry, scientific-computing, scientific-machine-learning, surrogate-models
- Homepage:
- Size: 4.34 MB
- Stars: 278
- Watchers: 5
- Forks: 21
- Open Issues: 89
-
Metadata Files:
- Readme: README.md
- Changelog: history/2023-06-09_changes.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
Awesome Lists containing this project
- ultimate-awesome - best-of-atomistic-machine-learning - 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎. (Programming Language Lists / Python Lists)
README
Best of Atomistic Machine Learning ⚛️🧬💎
🏆 A ranked list of awesome atomistic machine learning (AML) projects. Updated quarterly.
This curated list contains 360 awesome open-source projects with a total of 190K stars grouped into 22 categories. All projects are ranked by a [project-quality score](https://github.com/best-of-lists/best-of-generator#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](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose), submit a [pull request](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/pulls), or directly edit the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/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!
> 🧙♂️ Discover other [best-of lists](https://best-of.org) or [create your own](https://github.com/best-of-lists/best-of/blob/main/create-best-of-list.md).
## Contents
- [Active learning](#active-learning) _5 projects_
- [Biomolecules](#biomolecules) _2 projects_
- [Community resources](#community-resources) _21 projects_
- [Datasets](#datasets) _35 projects_
- [Data Structures](#data-structures) _4 projects_
- [Density functional theory (ML-DFT)](#density-functional-theory-ml-dft) _26 projects_
- [Educational Resources](#educational-resources) _24 projects_
- [Explainable Artificial intelligence (XAI)](#explainable-artificial-intelligence-xai) _4 projects_
- [Electronic structure methods (ML-ESM)](#electronic-structure-methods-ml-esm) _3 projects_
- [General Tools](#general-tools) _22 projects_
- [Generative Models](#generative-models) _11 projects_
- [Interatomic Potentials (ML-IAP)](#interatomic-potentials-ml-iap) _65 projects_
- [Language Models](#language-models) _17 projects_
- [Materials Discovery](#materials-discovery) _9 projects_
- [Mathematical tools](#mathematical-tools) _11 projects_
- [Molecular Dynamics](#molecular-dynamics) _10 projects_
- [Reinforcement Learning](#reinforcement-learning) _2 projects_
- [Representation Engineering](#representation-engineering) _23 projects_
- [Representation Learning](#representation-learning) _54 projects_
- [Unsupervised Learning](#unsupervised-learning) _7 projects_
- [Visualization](#visualization) _3 projects_
- [Wavefunction methods (ML-WFT)](#wavefunction-methods-ml-wft) _4 projects_
- [Others](#others) _2 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._
FLARE (🥇20 · ⭐ 280) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP
- [GitHub](https://github.com/mir-group/flare) (👨💻 36 · 🔀 63 · 📥 7 · 📦 11 · 📋 200 - 15% open · ⏱️ 11.06.2024):
```
git clone https://github.com/mir-group/flare
```
IPSuite (🥈15 · ⭐ 16) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. EPL-2.0 ML-IAP MD workflows HTC
- [GitHub](https://github.com/zincware/IPSuite) (👨💻 8 · 🔀 7 · 📦 2 · 📋 120 - 50% open · ⏱️ 16.07.2024):
```
git clone https://github.com/zincware/IPSuite
```
- [PyPi](https://pypi.org/project/ipsuite) (📥 66 / month):
```
pip install ipsuite
```
Finetuna (🥉10 · ⭐ 42) - Active Learning for Machine Learning Potentials. MIT
- [GitHub](https://github.com/ulissigroup/finetuna) (👨💻 11 · 🔀 11 · 📋 20 - 25% open · ⏱️ 15.05.2024):
```
git clone https://github.com/ulissigroup/finetuna
```
ACEHAL (🥉5 · ⭐ 11 · 💤) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia
- [GitHub](https://github.com/ACEsuit/ACEHAL) (👨💻 3 · 🔀 6 · 📋 10 - 40% open · ⏱️ 21.09.2023):
```
git clone https://github.com/ACEsuit/ACEHAL
```
Show 1 hidden projects...
- flare++ (🥉10 · ⭐ 35 · 💀) - A many-body extension of the FLARE code. MIT C++ ML-IAP
## Biomolecules
_Projects that focus on biomolecules, protein structure, protein folding, etc. using atomistic ML._
AlphaFold (🥇22 · ⭐ 12K) - Open source code for AlphaFold. Apache-2
- [GitHub](https://github.com/google-deepmind/alphafold) (👨💻 20 · 🔀 2.1K · 📦 14 · 📋 840 - 28% open · ⏱️ 08.05.2024):
```
git clone https://github.com/deepmind/alphafold
```
Uni-Fold (🥉16 · ⭐ 360) - An open-source platform for developing protein models beyond AlphaFold. Apache-2
- [GitHub](https://github.com/dptech-corp/Uni-Fold) (👨💻 7 · 🔀 65 · 📥 3.5K · 📋 70 - 27% open · ⏱️ 08.01.2024):
```
git clone https://github.com/dptech-corp/Uni-Fold
```
## 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 pre-trained transformer
🔗 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.
Best-of Machine Learning with Python (🥇22 · ⭐ 16K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨💻 46 · 🔀 2.3K · 📋 53 - 35% open · ⏱️ 22.07.2024):
```
git clone https://github.com/ml-tooling/best-of-ml-python
```
MatBench (🥇18 · ⭐ 100) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking
- [GitHub](https://github.com/materialsproject/matbench) (👨💻 25 · 🔀 41 · 📦 15 · 📋 57 - 54% open · ⏱️ 20.01.2024):
```
git clone https://github.com/materialsproject/matbench
```
- [PyPi](https://pypi.org/project/matbench) (📥 700 / month):
```
pip install matbench
```
GT4SD - Generative Toolkit for Scientific Discovery (🥈17 · ⭐ 320) - Gradio apps of generative models in GT4SD. MIT generative pre-trained drug-discovery
- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨💻 20 · 🔀 66 · 📋 99 - 1% open · ⏱️ 04.07.2024):
```
git clone https://github.com/GT4SD/gt4sd-core
```
MatBench Discovery (🥈17 · ⭐ 78 · 📈) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking
- [GitHub](https://github.com/janosh/matbench-discovery) (👨💻 7 · 🔀 10 · 📦 2 · 📋 33 - 6% open · ⏱️ 22.07.2024):
```
git clone https://github.com/janosh/matbench-discovery
```
- [PyPi](https://pypi.org/project/matbench-discovery) (📥 170 / month):
```
pip install matbench-discovery
```
Graph-based Deep Learning Literature (🥈16 · ⭐ 4.7K) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn
- [GitHub](https://github.com/naganandy/graph-based-deep-learning-literature) (👨💻 12 · 🔀 740 · ⏱️ 30.03.2024):
```
git clone https://github.com/naganandy/graph-based-deep-learning-literature
```
AI for Science Resources (🥈12 · ⭐ 460) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
- [GitHub](https://github.com/divelab/AIRS) (👨💻 28 · 🔀 56 · 📋 13 - 7% open · ⏱️ 12.07.2024):
```
git clone https://github.com/divelab/AIRS
```
GNoME Explorer (🥉10 · ⭐ 840 · 💤) - Graph Networks for Materials Exploration Database. Apache-2 datasets materials-discovery
- [GitHub](https://github.com/google-deepmind/materials_discovery) (👨💻 2 · 🔀 130 · 📋 20 - 80% open · ⏱️ 02.12.2023):
```
git clone https://github.com/google-deepmind/materials_discovery
```
Awesome Materials Informatics (🥉10 · ⭐ 350) - Curated list of known efforts in materials informatics, i.e. in modern materials science. Custom
- [GitHub](https://github.com/tilde-lab/awesome-materials-informatics) (👨💻 19 · 🔀 79 · ⏱️ 12.07.2024):
```
git clone https://github.com/tilde-lab/awesome-materials-informatics
```
MoLFormers UI (🥉9 · ⭐ 220 · 💤) - A family of foundation models trained on chemicals. Apache-2 transformer language-models pre-trained drug-discovery
- [GitHub](https://github.com/IBM/molformer) (👨💻 5 · 🔀 40 · 📋 18 - 44% open · ⏱️ 16.10.2023):
```
git clone https://github.com/IBM/molformer
```
optimade.science (🥉9 · ⭐ 8) - A sky-scanner Optimade browser-only GUI. MIT datasets
- [GitHub](https://github.com/tilde-lab/optimade.science) (👨💻 8 · 🔀 2 · 📋 26 - 26% open · ⏱️ 10.06.2024):
```
git clone https://github.com/tilde-lab/optimade.science
```
Awesome Neural Geometry (🥉8 · ⭐ 870) - 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) (👨💻 11 · 🔀 55 · ⏱️ 17.07.2024):
```
git clone https://github.com/neurreps/awesome-neural-geometry
```
The Collection of Database and Dataset Resources in Materials Science (🥉6 · ⭐ 240) - 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) (👨💻 2 · 🔀 40 · 📋 2 - 50% open · ⏱️ 07.06.2024):
```
git clone https://github.com/sedaoturak/data-resources-for-materials-science
```
Does this material exist? (🥉5 · ⭐ 15) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery
- [GitHub](https://github.com/ml-evs/this-material-does-not-exist) (👨💻 2 · 🔀 3 · ⏱️ 10.04.2024):
```
git clone https://github.com/ml-evs/this-material-does-not-exist
```
Show 3 hidden projects...
- A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉8 · ⭐ 110 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
- MateriApps (🥉1) - A Portal Site of Materials Science Simulation. Unlicensed
- GitHub topic materials-informatics - Unlicensed
## Datasets
_Datasets, databases and trained models for atomistic ML._
🔗 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. pre-trained language-models
🔗 JARVIS-Leaderboard ( ⭐ 54) - Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w. benchmarking community-resource educational
🔗 Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.
🔗 matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.
🔗 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
OPTIMADE Python tools (🥇25 · ⭐ 64) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT
- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨💻 28 · 🔀 41 · 📦 42 · 📋 440 - 20% open · ⏱️ 22.07.2024):
```
git clone https://github.com/Materials-Consortia/optimade-python-tools
```
- [PyPi](https://pypi.org/project/optimade) (📥 8.5K / month):
```
pip install optimade
```
- [Conda](https://anaconda.org/conda-forge/optimade) (📥 81K · ⏱️ 20.07.2024):
```
conda install -c conda-forge optimade
```
MPContribs (🥇22 · ⭐ 34) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
- [GitHub](https://github.com/materialsproject/MPContribs) (👨💻 25 · 🔀 19 · 📦 38 · 📋 98 - 20% open · ⏱️ 20.06.2024):
```
git clone https://github.com/materialsproject/MPContribs
```
- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 1.9K / month):
```
pip install mpcontribs-client
```
FAIR Chemistry datasets (🥇21 · ⭐ 730) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis
- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨💻 40 · 🔀 230 · 📋 190 - 2% open · ⏱️ 22.07.2024):
```
git clone https://github.com/FAIR-Chem/fairchem
```
Open Databases Integration for Materials Design (OPTIMADE) (🥈18 · ⭐ 75) - Specification of a common REST API for access to materials databases. CC-BY-4.0
- [GitHub](https://github.com/Materials-Consortia/OPTIMADE) (👨💻 21 · 🔀 35 · 📋 230 - 27% open · ⏱️ 12.06.2024):
```
git clone https://github.com/Materials-Consortia/OPTIMADE
```
QH9: A Quantum Hamiltonian Prediction Benchmark (🥈12 · ⭐ 460) - Artificial Intelligence Research for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
- [GitHub](https://github.com/divelab/AIRS) (👨💻 28 · 🔀 56 · 📋 13 - 7% open · ⏱️ 12.07.2024):
```
git clone https://github.com/divelab/AIRS
```
SPICE (🥈12 · ⭐ 140) - A collection of QM data for training potential functions. MIT ML-IAP MD
- [GitHub](https://github.com/openmm/spice-dataset) (🔀 7 · 📥 240 · 📋 59 - 25% open · ⏱️ 15.04.2024):
```
git clone https://github.com/openmm/spice-dataset
```
Materials Data Facility (MDF) (🥈10 · ⭐ 10) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2
- [GitHub](https://github.com/materials-data-facility/connect_client) (👨💻 7 · 🔀 1 · 📋 7 - 14% open · ⏱️ 05.02.2024):
```
git clone https://github.com/materials-data-facility/connect_client
```
2DMD dataset (🥉9 · ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 material-defect
- [GitHub](https://github.com/HSE-LAMBDA/ai4material_design) (👨💻 11 · 🔀 3 · ⏱️ 21.11.2023):
```
git clone https://github.com/HSE-LAMBDA/ai4material_design
```
3DSC Database (🥉5 · ⭐ 14) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom superconductors materials-discovery
- [GitHub](https://github.com/aimat-lab/3DSC) (🔀 4 · ⏱️ 08.01.2024):
```
git clone https://github.com/aimat-lab/3DSC
```
SciGlass (🥉5 · ⭐ 10 · 💤) - The database contains a vast set of data on the properties of glass materials. MIT
- [GitHub](https://github.com/drcassar/SciGlass) (👨💻 2 · 🔀 3 · 📥 16 · ⏱️ 27.08.2023):
```
git clone https://github.com/drcassar/SciGlass
```
Show 12 hidden projects...
- ATOM3D (🥈18 · ⭐ 290 · 💀) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking
- OpenKIM (🥈10 · ⭐ 31 · 💀) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 knowledge-base pre-trained
- ANI-1 Dataset (🥉8 · ⭐ 95 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
- MoleculeNet Leaderboard (🥉8 · ⭐ 85 · 💀) - MIT benchmarking
- GEOM (🥉7 · ⭐ 190 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
- ANI-1x Datasets (🥉6 · ⭐ 53 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
- COMP6 Benchmark dataset (🥉6 · ⭐ 39 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT
- OPTIMADE providers dashboard (🥉6 · ⭐ 1) - A dashboard of known providers. Unlicensed
- Visual Graph Datasets (🥉5 · ⭐ 1) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT
- linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper
- paper-data-redundancy (🥉4 · ⭐ 7) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper
- nep-data (🥉2 · ⭐ 12 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena
## Data Structures
_Projects that focus on providing data structures used in atomistic machine learning._
dpdata (🥇24 · ⭐ 190) - Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc. LGPL-3.0
- [GitHub](https://github.com/deepmodeling/dpdata) (👨💻 60 · 🔀 120 · 📦 120 · 📋 94 - 18% open · ⏱️ 06.06.2024):
```
git clone https://github.com/deepmodeling/dpdata
```
- [PyPi](https://pypi.org/project/dpdata) (📥 45K / month):
```
pip install dpdata
```
- [Conda](https://anaconda.org/deepmodeling/dpdata) (📥 210 · ⏱️ 27.09.2023):
```
conda install -c deepmodeling dpdata
```
Metatensor (🥈21 · ⭐ 45) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 Rust C-lang C++ Python
- [GitHub](https://github.com/lab-cosmo/metatensor) (👨💻 21 · 🔀 13 · 📥 24K · 📦 11 · 📋 190 - 34% open · ⏱️ 22.07.2024):
```
git clone https://github.com/lab-cosmo/metatensor
```
mp-pyrho (🥉17 · ⭐ 36) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT
- [GitHub](https://github.com/materialsproject/pyrho) (👨💻 8 · 🔀 6 · 📦 22 · 📋 4 - 25% open · ⏱️ 23.02.2024):
```
git clone https://github.com/materialsproject/pyrho
```
- [PyPi](https://pypi.org/project/mp-pyrho) (📥 6.5K / month):
```
pip install mp-pyrho
```
dlpack (🥉15 · ⭐ 870) - common in-memory tensor structure. Apache-2 C++
- [GitHub](https://github.com/dmlc/dlpack) (👨💻 23 · 🔀 130 · 📋 69 - 39% open · ⏱️ 26.03.2024):
```
git clone https://github.com/dmlc/dlpack
```
## 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._
JAX-DFT (🥇25 · ⭐ 33K) - 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) (👨💻 790 · 🔀 7.7K · 📋 1.2K - 73% open · ⏱️ 22.07.2024):
```
git clone https://github.com/google-research/google-research
```
MALA (🥇20 · ⭐ 80) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3
- [GitHub](https://github.com/mala-project/mala) (👨💻 44 · 🔀 23 · 📦 1 · 📋 260 - 12% open · ⏱️ 04.07.2024):
```
git clone https://github.com/mala-project/mala
```
SALTED (🥈14 · ⭐ 27) - Symmetry-Adapted Learning of Three-dimensional Electron Densities. GPL-3.0
- [GitHub](https://github.com/andreagrisafi/SALTED) (👨💻 16 · 🔀 4 · 📋 6 - 16% open · ⏱️ 08.07.2024):
```
git clone https://github.com/andreagrisafi/SALTED
```
DeepH-pack (🥈13 · ⭐ 200) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0 Julia
- [GitHub](https://github.com/mzjb/DeepH-pack) (👨💻 9 · 🔀 36 · 📋 50 - 24% open · ⏱️ 22.05.2024):
```
git clone https://github.com/mzjb/DeepH-pack
```
QHNet (🥈12 · ⭐ 460) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn
- [GitHub](https://github.com/divelab/AIRS) (👨💻 28 · 🔀 56 · 📋 13 - 7% open · ⏱️ 12.07.2024):
```
git clone https://github.com/divelab/AIRS
```
DeePKS-kit (🥈10 · ⭐ 96) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0
- [GitHub](https://github.com/deepmodeling/deepks-kit) (👨💻 7 · 🔀 33 · 📋 18 - 27% open · ⏱️ 13.04.2024):
```
git clone https://github.com/deepmodeling/deepks-kit
```
Grad DFT (🥈10 · ⭐ 71) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
- [GitHub](https://github.com/XanaduAI/GradDFT) (👨💻 4 · 🔀 5 · 📋 54 - 20% open · ⏱️ 13.02.2024):
```
git clone https://github.com/XanaduAI/GradDFT
```
Show 19 hidden projects...
- DM21 (🥇20 · ⭐ 13K · 💀) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. Apache-2
- NeuralXC (🥈11 · ⭐ 33 · 💀) - Implementation of a machine learned density functional. BSD-3
- ACEhamiltonians (🥈11 · ⭐ 12 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia
- PROPhet (🥈9 · ⭐ 62 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
- Libnxc (🥉7 · ⭐ 15 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
- DeepH-E3 (🥉6 · ⭐ 60 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism
- DeepDFT (🥉6 · ⭐ 54 · 💀) - Official implementation of DeepDFT model. MIT
- Mat2Spec (🥉6 · ⭐ 27 · 💀) - MIT spectroscopy
- xDeepH (🥉5 · ⭐ 30 · 💀) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
- ML-DFT (🥉5 · ⭐ 23 · 💀) - A package for density functional approximation using machine learning. MIT
- charge-density-models (🥉4 · ⭐ 10 · 💤) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn
- gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
- DeepCDP (🥉3 · ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
- APET (🥉3 · ⭐ 4 · 💤) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
- CSNN (🥉3 · ⭐ 2 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
- MALADA (🥉3 · ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
- A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed representation-learning single-paper
- kdft (🥉1 · ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed
- MLDensity (🥉1 · ⭐ 2 · 💀) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. Unlicensed
## Educational Resources
_Tutorials, guides, cookbooks, recipes, etc._
🔗 Quantum Chemistry in the Age of Machine Learning - Book, 2022.
🔗 AL4MS 2023 workshop tutorials active-learning
jarvis-tools-notebooks (🥇12 · ⭐ 52) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST
- [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨💻 5 · 🔀 25 · ⏱️ 02.07.2024):
```
git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks
```
Deep Learning for Molecules and Materials Book (🥇11 · ⭐ 600 · 💤) - Deep learning for molecules and materials book. Custom
- [GitHub](https://github.com/whitead/dmol-book) (👨💻 19 · 🔀 110 · 📋 160 - 17% open · ⏱️ 02.07.2023):
```
git clone https://github.com/whitead/dmol-book
```
DSECOP (🥈10 · ⭐ 43) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
- [GitHub](https://github.com/GDS-Education-Community-of-Practice/DSECOP) (👨💻 13 · 🔀 25 · 📋 8 - 12% open · ⏱️ 26.06.2024):
```
git clone https://github.com/GDS-Education-Community-of-Practice/DSECOP
```
iam-notebooks (🥈10 · ⭐ 24) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2
- [GitHub](https://github.com/ceriottm/iam-notebooks) (👨💻 6 · 🔀 5 · ⏱️ 26.06.2024):
```
git clone https://github.com/ceriottm/iam-notebooks
```
OPTIMADE Tutorial Exercises (🥈9 · ⭐ 14 · 💤) - Tutorial exercises for the OPTIMADE API. MIT datasets
- [GitHub](https://github.com/Materials-Consortia/optimade-tutorial-exercises) (👨💻 6 · 🔀 7 · ⏱️ 27.09.2023):
```
git clone https://github.com/Materials-Consortia/optimade-tutorial-exercises
```
BestPractices (🥈8 · ⭐ 160 · 💤) - Things that you should (and should not) do in your Materials Informatics research. MIT
- [GitHub](https://github.com/anthony-wang/BestPractices) (👨💻 3 · 🔀 68 · 📋 7 - 71% open · ⏱️ 17.11.2023):
```
git clone https://github.com/anthony-wang/BestPractices
```
COSMO Software Cookbook (🥉7 · ⭐ 10) - The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules. BSD-3
- [GitHub](https://github.com/lab-cosmo/software-cookbook) (👨💻 9 · 🔀 1 · 📋 12 - 16% open · ⏱️ 29.06.2024):
```
git clone https://github.com/lab-cosmo/software-cookbook
```
MACE-tutorials (🥉6 · ⭐ 30) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
- [GitHub](https://github.com/ilyes319/mace-tutorials) (👨💻 2 · 🔀 8 · ⏱️ 16.07.2024):
```
git clone https://github.com/ilyes319/mace-tutorials
```
Show 14 hidden projects...
- Geometric GNN Dojo (🥇13 · ⭐ 440 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
- DeepLearningLifeSciences (🥇11 · ⭐ 340 · 💀) - Example code from the book Deep Learning for the Life Sciences. MIT
- RDKit Tutorials (🥈8 · ⭐ 250 · 💀) - Tutorials to learn how to work with the RDKit. Custom
- MAChINE (🥉7 · ⭐ 1 · 💤) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT
- Applied AI for Materials (🥉6 · ⭐ 58 · 💀) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed
- AI4Science101 (🥉5 · ⭐ 83 · 💀) - AI for Science. Unlicensed
- Machine Learning for Materials Hard and Soft (🥉5 · ⭐ 34 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
- Data Handling, DoE and Statistical Analysis for Material Chemists (🥉5 · ⭐ 1 · 💀) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. GPL-3.0
- ML-in-chemistry-101 (🥉4 · ⭐ 65 · 💀) - The course materials for Machine Learning in Chemistry 101. Unlicensed
- chemrev-gpr (🥉4 · ⭐ 6 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
- MLDensity_tutorial (🥉2 · ⭐ 7 · 💀) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
- LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 3 · 💀) - 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 (🥉2 · ⭐ 2 · 💤) - A full MALA hands-on tutorial. Unlicensed
- PiNN Lab (🥉2 · ⭐ 2 · 💀) - GPL-3.0
## Explainable Artificial intelligence (XAI)
_Projects that focus on explainability and model interpretability in atomistic ML._
exmol (🥇19 · ⭐ 280 · 💤) - Explainer for black box models that predict molecule properties. MIT
- [GitHub](https://github.com/ur-whitelab/exmol) (👨💻 7 · 🔀 40 · 📦 20 · 📋 69 - 15% open · ⏱️ 04.12.2023):
```
git clone https://github.com/ur-whitelab/exmol
```
- [PyPi](https://pypi.org/project/exmol) (📥 600 / month):
```
pip install exmol
```
MEGAN: Multi Explanation Graph Attention Student (🥈6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT
- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨💻 2 · 🔀 1 · ⏱️ 08.07.2024):
```
git clone https://github.com/aimat-lab/graph_attention_student
```
MEGAN (🥈6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT XAI rep-learn
- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨💻 2 · 🔀 1 · ⏱️ 08.07.2024):
```
git clone https://github.com/aimat-lab/graph_attention_student
```
Show 1 hidden projects...
- Linear vs blackbox (🥉3 · ⭐ 2 · 💀) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng
## 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._
Show 3 hidden projects...
- QDF for molecule (🥇9 · ⭐ 190 · 💀) - Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation.. MIT
- halex (🥈4 · ⭐ 2) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed excited-states
- e3psi (🥉3 · ⭐ 3) - Equivariant machine learning library for learning from electronic structures. LGPL-3.0
## General Tools
_General tools for atomistic machine learning._
DeepChem (🥇35 · ⭐ 5.3K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT
- [GitHub](https://github.com/deepchem/deepchem) (👨💻 250 · 🔀 1.6K · 📦 400 · 📋 1.7K - 26% open · ⏱️ 10.07.2024):
```
git clone https://github.com/deepchem/deepchem
```
- [PyPi](https://pypi.org/project/deepchem) (📥 29K / month):
```
pip install deepchem
```
- [Conda](https://anaconda.org/conda-forge/deepchem) (📥 110K · ⏱️ 05.04.2024):
```
conda install -c conda-forge deepchem
```
- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.3K · ⭐ 5 · ⏱️ 10.07.2024):
```
docker pull deepchemio/deepchem
```
RDKit (🥇32 · ⭐ 2.5K) - BSD-3 C++
- [GitHub](https://github.com/rdkit/rdkit) (👨💻 230 · 🔀 830 · 📥 1.2K · 📦 3 · 📋 3.2K - 28% open · ⏱️ 23.07.2024):
```
git clone https://github.com/rdkit/rdkit
```
- [PyPi](https://pypi.org/project/rdkit) (📥 640K / month):
```
pip install rdkit
```
- [Conda](https://anaconda.org/rdkit/rdkit) (📥 2.6M · ⏱️ 16.06.2023):
```
conda install -c rdkit rdkit
```
Matminer (🥇27 · ⭐ 450 · 📉) - Data mining for materials science. Custom
- [GitHub](https://github.com/hackingmaterials/matminer) (👨💻 54 · 🔀 180 · 📦 300 · 📋 220 - 10% open · ⏱️ 27.05.2024):
```
git clone https://github.com/hackingmaterials/matminer
```
- [PyPi](https://pypi.org/project/matminer) (📥 10K / month):
```
pip install matminer
```
- [Conda](https://anaconda.org/conda-forge/matminer) (📥 65K · ⏱️ 28.03.2024):
```
conda install -c conda-forge matminer
```
QUIP (🥈25 · ⭐ 350) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran
- [GitHub](https://github.com/libAtoms/QUIP) (👨💻 84 · 🔀 120 · 📥 510 · 📦 38 · 📋 460 - 22% open · ⏱️ 19.07.2024):
```
git clone https://github.com/libAtoms/QUIP
```
- [PyPi](https://pypi.org/project/quippy-ase) (📥 3.1K / month):
```
pip install quippy-ase
```
- [Docker Hub](https://hub.docker.com/r/libatomsquip/quip) (📥 9.9K · ⭐ 4 · ⏱️ 24.04.2023):
```
docker pull libatomsquip/quip
```
MAML (🥈24 · ⭐ 340) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3
- [GitHub](https://github.com/materialsvirtuallab/maml) (👨💻 32 · 🔀 73 · 📦 9 · 📋 68 - 10% open · ⏱️ 03.07.2024):
```
git clone https://github.com/materialsvirtuallab/maml
```
- [PyPi](https://pypi.org/project/maml) (📥 250 / month):
```
pip install maml
```
JARVIS-Tools (🥈24 · ⭐ 280) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom
- [GitHub](https://github.com/usnistgov/jarvis) (👨💻 15 · 🔀 120 · 📦 92 · 📋 89 - 49% open · ⏱️ 28.06.2024):
```
git clone https://github.com/usnistgov/jarvis
```
- [PyPi](https://pypi.org/project/jarvis-tools) (📥 16K / month):
```
pip install jarvis-tools
```
- [Conda](https://anaconda.org/conda-forge/jarvis-tools) (📥 71K · ⏱️ 28.06.2024):
```
conda install -c conda-forge jarvis-tools
```
MAST-ML (🥈19 · ⭐ 98) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT
- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨💻 18 · 🔀 57 · 📥 95 · 📦 43 · 📋 210 - 10% open · ⏱️ 17.04.2024):
```
git clone https://github.com/uw-cmg/MAST-ML
```
Scikit-Matter (🥈19 · ⭐ 70) - 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) (👨💻 15 · 🔀 18 · 📦 10 · 📋 69 - 18% open · ⏱️ 15.06.2024):
```
git clone https://github.com/scikit-learn-contrib/scikit-matter
```
- [PyPi](https://pypi.org/project/skmatter) (📥 1.3K / month):
```
pip install skmatter
```
- [Conda](https://anaconda.org/conda-forge/skmatter) (📥 1.8K · ⏱️ 24.08.2023):
```
conda install -c conda-forge skmatter
```
XenonPy (🥈16 · ⭐ 130 · 📈) - XenonPy is a Python Software for Materials Informatics. BSD-3
- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨💻 10 · 🔀 57 · 📥 1.4K · 📋 86 - 23% open · ⏱️ 21.04.2024):
```
git clone https://github.com/yoshida-lab/XenonPy
```
- [PyPi](https://pypi.org/project/xenonpy) (📥 220 / month):
```
pip install xenonpy
```
Artificial Intelligence for Science (AIRS) (🥉12 · ⭐ 460) - 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) (👨💻 28 · 🔀 56 · 📋 13 - 7% open · ⏱️ 12.07.2024):
```
git clone https://github.com/divelab/AIRS
```
AMPtorch (🥉11 · ⭐ 58 · 💤) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0
- [GitHub](https://github.com/ulissigroup/amptorch) (👨💻 14 · 🔀 32 · 📋 33 - 21% open · ⏱️ 16.07.2023):
```
git clone https://github.com/ulissigroup/amptorch
```
Equisolve (🥉6 · ⭐ 5 · 💤) - A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties.. BSD-3 ML-IAP
- [GitHub](https://github.com/lab-cosmo/equisolve) (👨💻 6 · 🔀 1 · 📋 23 - 82% open · ⏱️ 27.10.2023):
```
git clone https://github.com/lab-cosmo/equisolve
```
Show 10 hidden projects...
- QML (🥉15 · ⭐ 200 · 💀) - QML: Quantum Machine Learning. MIT
- Automatminer (🥉15 · ⭐ 130 · 💀) - An automatic engine for predicting materials properties. Custom
- OpenChem (🥉10 · ⭐ 670 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT
- JAXChem (🥉7 · ⭐ 74 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT
- uncertainty_benchmarking (🥉7 · ⭐ 38 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic
- torchchem (🥉7 · ⭐ 34 · 💀) - An experimental repo for experimenting with PyTorch models. MIT
- ACEatoms (🥉4 · ⭐ 2 · 💀) - Generic code for modelling atomic properties using ACE. Custom Julia
- MLatom (🥉4) - Machine learning for atomistic simulations. Custom
- Magpie (🥉3) - Materials Agnostic Platform for Informatics and Exploration (Magpie). MIT Java
- quantum-structure-ml (🥉2 · ⭐ 2 · 💀) - Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification.. Unlicensed magnetism benchmarking
## Generative Models
_Projects that implement generative models for atomistic ML._
GT4SD (🥇19 · ⭐ 320) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pre-trained drug-discovery rep-learn
- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨💻 20 · 🔀 66 · 📋 99 - 1% open · ⏱️ 04.07.2024):
```
git clone https://github.com/GT4SD/gt4sd-core
```
- [PyPi](https://pypi.org/project/gt4sd) (📥 730 / month):
```
pip install gt4sd
```
MoLeR (🥇15 · ⭐ 250) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT
- [GitHub](https://github.com/microsoft/molecule-generation) (👨💻 5 · 🔀 36 · 📋 37 - 24% open · ⏱️ 03.01.2024):
```
git clone https://github.com/microsoft/molecule-generation
```
- [PyPi](https://pypi.org/project/molecule-generation) (📥 380 / month):
```
pip install molecule-generation
```
SchNetPack G-SchNet (🥈14 · ⭐ 42) - G-SchNet extension for SchNetPack. MIT
- [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (👨💻 3 · 🔀 8 · ⏱️ 05.07.2024):
```
git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet
```
bVAE-IM (🥉8 · ⭐ 11 · 💤) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper
- [GitHub](https://github.com/tsudalab/bVAE-IM) (🔀 3 · ⏱️ 11.07.2023):
```
git clone https://github.com/tsudalab/bVAE-IM
```
COATI (🥉5 · ⭐ 87) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery pre-trained rep-learn
- [GitHub](https://github.com/terraytherapeutics/COATI) (👨💻 5 · 🔀 5 · 📋 3 - 33% open · ⏱️ 23.03.2024):
```
git clone https://github.com/terraytherapeutics/COATI
```
Show 6 hidden projects...
- synspace (🥈12 · ⭐ 35 · 💀) - Synthesis generative model. MIT
- EDM (🥈10 · ⭐ 410 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
- G-SchNet (🥉8 · ⭐ 130 · 💀) - G-SchNet - a generative model for 3d molecular structures. MIT
- cG-SchNet (🥉8 · ⭐ 47 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT
- rxngenerator (🥉5 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT
- MolSLEPA (🥉5 · ⭐ 5 · 💀) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI
## Interatomic Potentials (ML-IAP)
_Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics._
DeePMD-kit (🥇27 · ⭐ 1.4K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++
- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨💻 69 · 🔀 470 · 📥 37K · 📦 16 · 📋 730 - 10% open · ⏱️ 06.07.2024):
```
git clone https://github.com/deepmodeling/deepmd-kit
```
- [PyPi](https://pypi.org/project/deepmd-kit) (📥 1.8K / month):
```
pip install deepmd-kit
```
- [Conda](https://anaconda.org/deepmodeling/deepmd-kit) (📥 1.2K · ⏱️ 06.04.2024):
```
conda install -c deepmodeling deepmd-kit
```
- [Docker Hub](https://hub.docker.com/r/deepmodeling/deepmd-kit) (📥 2.6K · ⭐ 1 · ⏱️ 08.07.2024):
```
docker pull deepmodeling/deepmd-kit
```
NequIP (🥇24 · ⭐ 570) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT
- [GitHub](https://github.com/mir-group/nequip) (👨💻 11 · 🔀 120 · 📦 19 · 📋 87 - 24% open · ⏱️ 09.07.2024):
```
git clone https://github.com/mir-group/nequip
```
- [PyPi](https://pypi.org/project/nequip) (📥 2.8K / month):
```
pip install nequip
```
- [Conda](https://anaconda.org/conda-forge/nequip) (📥 5K · ⏱️ 10.07.2024):
```
conda install -c conda-forge nequip
```
TorchANI (🥇22 · ⭐ 450 · 💤) - Accurate Neural Network Potential on PyTorch. MIT
- [GitHub](https://github.com/aiqm/torchani) (👨💻 17 · 🔀 120 · 📦 39 · 📋 170 - 13% open · ⏱️ 14.11.2023):
```
git clone https://github.com/aiqm/torchani
```
- [PyPi](https://pypi.org/project/torchani) (📥 4.8K / month):
```
pip install torchani
```
- [Conda](https://anaconda.org/conda-forge/torchani) (📥 370K · ⏱️ 31.05.2024):
```
conda install -c conda-forge torchani
```
MACE (🥇22 · ⭐ 430) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT
- [GitHub](https://github.com/ACEsuit/mace) (👨💻 34 · 🔀 160 · 📋 210 - 21% open · ⏱️ 16.07.2024):
```
git clone https://github.com/ACEsuit/mace
```
GPUMD (🥇22 · ⭐ 420) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics
- [GitHub](https://github.com/brucefan1983/GPUMD) (👨💻 29 · 🔀 110 · 📋 170 - 10% open · ⏱️ 09.07.2024):
```
git clone https://github.com/brucefan1983/GPUMD
```
TorchMD-NET (🥇22 · ⭐ 300) - Training neural network potentials. MIT MD rep-learn transformer pre-trained
- [GitHub](https://github.com/torchmd/torchmd-net) (👨💻 16 · 🔀 69 · 📋 100 - 20% open · ⏱️ 09.07.2024):
```
git clone https://github.com/torchmd/torchmd-net
```
- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 85K · ⏱️ 09.07.2024):
```
conda install -c conda-forge torchmd-net
```
DP-GEN (🥇22 · ⭐ 290) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 workflows
- [GitHub](https://github.com/deepmodeling/dpgen) (👨💻 64 · 🔀 170 · 📥 1.7K · 📦 6 · 📋 290 - 10% open · ⏱️ 10.04.2024):
```
git clone https://github.com/deepmodeling/dpgen
```
- [PyPi](https://pypi.org/project/dpgen) (📥 460 / month):
```
pip install dpgen
```
- [Conda](https://anaconda.org/deepmodeling/dpgen) (📥 200 · ⏱️ 16.06.2023):
```
conda install -c deepmodeling dpgen
```
CHGNet (🥇22 · ⭐ 220) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom MD pre-trained electrostatics magnetism structure-relaxation
- [GitHub](https://github.com/CederGroupHub/chgnet) (👨💻 7 · 🔀 55 · 📦 25 · 📋 50 - 6% open · ⏱️ 07.07.2024):
```
git clone https://github.com/CederGroupHub/chgnet
```
- [PyPi](https://pypi.org/project/chgnet) (📥 21K / month):
```
pip install chgnet
```
fairchem (🥈19 · ⭐ 730) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp). Unlicensed pre-trained rep-learn catalysis
- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨💻 40 · 🔀 230 · 📋 190 - 2% open · ⏱️ 22.07.2024):
```
git clone https://github.com/FAIR-Chem/fairchem
```
Neural Force Field (🥈18 · ⭐ 220) - Neural Network Force Field based on PyTorch. MIT pre-trained
- [GitHub](https://github.com/learningmatter-mit/NeuralForceField) (👨💻 40 · 🔀 46 · 📋 19 - 5% open · ⏱️ 18.07.2024):
```
git clone https://github.com/learningmatter-mit/NeuralForceField
```
Ultra-Fast Force Fields (UF3) (🥈15 · ⭐ 55) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2
- [GitHub](https://github.com/uf3/uf3) (👨💻 10 · 🔀 20 · 📦 1 · 📋 42 - 30% open · ⏱️ 12.07.2024):
```
git clone https://github.com/uf3/uf3
```
- [PyPi](https://pypi.org/project/uf3) (📥 29 / month):
```
pip install uf3
```
KLIFF (🥈15 · ⭐ 34) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows
- [GitHub](https://github.com/openkim/kliff) (👨💻 9 · 🔀 20 · 📦 3 · 📋 40 - 52% open · ⏱️ 06.07.2024):
```
git clone https://github.com/openkim/kliff
```
- [PyPi](https://pypi.org/project/kliff) (📥 60 / month):
```
pip install kliff
```
- [Conda](https://anaconda.org/conda-forge/kliff) (📥 90K · ⏱️ 18.12.2023):
```
conda install -c conda-forge kliff
```
wfl (🥈15 · ⭐ 23) - 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) (👨💻 18 · 🔀 17 · 📦 1 · 📋 150 - 42% open · ⏱️ 08.07.2024):
```
git clone https://github.com/libAtoms/workflow
```
sGDML (🥈14 · ⭐ 140 · 💤) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT
- [GitHub](https://github.com/stefanch/sGDML) (👨💻 8 · 🔀 36 · 📦 9 · 📋 17 - 35% open · ⏱️ 31.08.2023):
```
git clone https://github.com/stefanch/sGDML
```
- [PyPi](https://pypi.org/project/sgdml) (📥 120 / month):
```
pip install sgdml
```
PyXtalFF (🥈14 · ⭐ 85) - Machine Learning Interatomic Potential Predictions. MIT
- [GitHub](https://github.com/MaterSim/PyXtal_FF) (👨💻 9 · 🔀 23 · 📋 62 - 17% open · ⏱️ 07.01.2024):
```
git clone https://github.com/MaterSim/PyXtal_FF
```
- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 120 / month):
```
pip install pyxtal_ff
```
NNPOps (🥈13 · ⭐ 79) - High-performance operations for neural network potentials. MIT MD C++
- [GitHub](https://github.com/openmm/NNPOps) (👨💻 9 · 🔀 17 · 📋 55 - 38% open · ⏱️ 10.07.2024):
```
git clone https://github.com/openmm/NNPOps
```
- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 170K · ⏱️ 31.05.2024):
```
conda install -c conda-forge nnpops
```
apax (🥈13 · ⭐ 12) - A flexible and performant framework for training machine learning potentials. MIT
- [GitHub](https://github.com/apax-hub/apax) (👨💻 7 · 🔀 1 · 📦 2 · 📋 110 - 16% open · ⏱️ 17.05.2024):
```
git clone https://github.com/apax-hub/apax
```
- [PyPi](https://pypi.org/project/apax) (📥 84 / month):
```
pip install apax
```
CCS_fit (🥈13 · ⭐ 7) - Curvature Constrained Splines. GPL-3.0
- [GitHub](https://github.com/Teoroo-CMC/CCS) (👨💻 8 · 🔀 10 · 📥 410 · 📋 14 - 57% open · ⏱️ 16.02.2024):
```
git clone https://github.com/Teoroo-CMC/CCS
```
- [PyPi](https://pypi.org/project/ccs_fit) (📥 380 / month):
```
pip install ccs_fit
```
ANI-1 (🥈12 · ⭐ 220) - ANI-1 neural net potential with python interface (ASE). MIT
- [GitHub](https://github.com/isayev/ASE_ANI) (👨💻 6 · 🔀 55 · 📋 37 - 43% open · ⏱️ 11.03.2024):
```
git clone https://github.com/isayev/ASE_ANI
```
DMFF (🥈12 · ⭐ 140) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0
- [GitHub](https://github.com/deepmodeling/DMFF) (👨💻 14 · 🔀 40 · 📋 26 - 38% open · ⏱️ 12.01.2024):
```
git clone https://github.com/deepmodeling/DMFF
```
Pacemaker (🥈12 · ⭐ 59) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom
- [GitHub](https://github.com/ICAMS/python-ace) (👨💻 5 · 🔀 15 · 📋 45 - 28% open · ⏱️ 22.07.2024):
```
git clone https://github.com/ICAMS/python-ace
```
- [PyPi](https://pypi.org/project/python-ace) (📥 25 / month):
```
pip install python-ace
```
PiNN (🥈11 · ⭐ 100) - A Python library for building atomic neural networks. BSD-3
- [GitHub](https://github.com/Teoroo-CMC/PiNN) (👨💻 5 · 🔀 31 · 📋 6 - 16% open · ⏱️ 27.06.2024):
```
git clone https://github.com/Teoroo-CMC/PiNN
```
- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (📥 240 · ⏱️ 27.06.2024):
```
docker pull teoroo/pinn
```
So3krates (MLFF) (🥈11 · ⭐ 58) - Build neural networks for machine learning force fields with JAX. MIT
- [GitHub](https://github.com/thorben-frank/mlff) (👨💻 4 · 🔀 13 · 📋 9 - 33% open · ⏱️ 04.07.2024):
```
git clone https://github.com/thorben-frank/mlff
```
tinker-hp (🥉10 · ⭐ 77) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom
- [GitHub](https://github.com/TinkerTools/tinker-hp) (👨💻 10 · 🔀 21 · 📋 19 - 15% open · ⏱️ 04.07.2024):
```
git clone https://github.com/TinkerTools/tinker-hp
```
Point Edge Transformer (PET) (🥉10 · ⭐ 16) - Point Edge Transformer. MIT rep-learn transformer
- [GitHub](https://github.com/spozdn/pet) (👨💻 7 · 🔀 5 · ⏱️ 02.07.2024):
```
git clone https://github.com/spozdn/pet
```
DimeNet (🥉9 · ⭐ 280 · 💤) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom
- [GitHub](https://github.com/gasteigerjo/dimenet) (👨💻 2 · 🔀 57 · 📦 1 · 📋 31 - 3% open · ⏱️ 03.10.2023):
```
git clone https://github.com/gasteigerjo/dimenet
```
TurboGAP (🥉9 · ⭐ 16) - The TurboGAP code. Custom Fortran
- [GitHub](https://github.com/mcaroba/turbogap) (👨💻 8 · 🔀 8 · 📋 9 - 66% open · ⏱️ 09.07.2024):
```
git clone https://github.com/mcaroba/turbogap
```
- [GitHub](https://github.com/ACEsuit/ACEfit.jl) (👨💻 7 · 🔀 5 · 📋 55 - 40% open · ⏱️ 28.03.2024):
```
git clone https://github.com/ACEsuit/ACEfit.jl
```
MACE-Jax (🥉8 · ⭐ 55 · 💤) - Equivariant machine learning interatomic potentials in JAX. MIT
- [GitHub](https://github.com/ACEsuit/mace-jax) (👨💻 2 · 🔀 3 · 📋 5 - 40% open · ⏱️ 04.10.2023):
```
git clone https://github.com/ACEsuit/mace-jax
```
PyNEP (🥉8 · ⭐ 42) - A python interface of the machine learning potential NEP used in GPUMD. MIT
- [GitHub](https://github.com/bigd4/PyNEP) (👨💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024):
```
git clone https://github.com/bigd4/PyNEP
```
GAP (🥉8 · ⭐ 39) - Gaussian Approximation Potential (GAP). Custom
- [GitHub](https://github.com/libAtoms/GAP) (👨💻 13 · 🔀 20 · ⏱️ 19.07.2024):
```
git clone https://github.com/libAtoms/GAP
```
ACE1.jl (🥉8 · ⭐ 20) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia
- [GitHub](https://github.com/ACEsuit/ACE1.jl) (👨💻 9 · 🔀 7 · 📋 46 - 47% open · ⏱️ 02.07.2024):
```
git clone https://github.com/ACEsuit/ACE1.jl
```
MLXDM (🥉8 · ⭐ 6) - 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) (👨💻 7 · 🔀 2 · ⏱️ 15.07.2024):
```
git clone https://github.com/RowleyGroup/MLXDM
```
ALF (🥉7 · ⭐ 28) - A framework for performing active learning for training machine-learned interatomic potentials. Custom active-learning
- [GitHub](https://github.com/lanl/ALF) (👨💻 5 · 🔀 11 · ⏱️ 28.05.2024):
```
git clone https://github.com/lanl/alf
```
ACE1Pack.jl (🥉6 · 💤) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT Julia
- [GitHub](https://github.com/ACEsuit/ACE1pack.jl) (👨💻 11 · ⏱️ 21.08.2023):
```
git clone https://github.com/ACEsuit/ACE1pack.jl
```
Allegro-JAX ( ⭐ 16) - JAX implementation of the Allegro interatomic potential. Unlicensed
- [GitHub](https://github.com/mariogeiger/allegro-jax) (👨💻 2 · 🔀 2 · ⏱️ 09.04.2024):
```
git clone https://github.com/mariogeiger/allegro-jax
```
Show 29 hidden projects...
- MEGNet (🥇22 · ⭐ 490 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3
- M3GNet (🥈17 · ⭐ 220 · 💀) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3
- n2p2 (🥈13 · ⭐ 210 · 💀) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++
- TensorMol (🥈12 · ⭐ 270 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
- SIMPLE-NN (🥈11 · ⭐ 47 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
- Allegro (🥉10 · ⭐ 300 · 💀) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT
- NNsforMD (🥉10 · ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT
- SchNet (🥉9 · ⭐ 210 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT
- GemNet (🥉9 · ⭐ 170 · 💀) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom
- ACE.jl (🥉9 · ⭐ 66 · 💀) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia
- AIMNet (🥉8 · ⭐ 92 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper
- SNAP (🥉8 · ⭐ 36 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
- Atomistic Adversarial Attacks (🥉8 · ⭐ 30 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic
- calorine (🥉8 · ⭐ 12 · 💀) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom
- PhysNet (🥉7 · ⭐ 89 · 💀) - Code for training PhysNet models. MIT electrostatics
- SIMPLE-NN v2 (🥉7 · ⭐ 38 · 💤) - GPL-3.0
- MLIP-3 (🥉6 · ⭐ 25 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++
- testing-framework (🥉6 · ⭐ 11 · 💀) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. Unlicensed benchmarking
- PANNA (🥉6 · ⭐ 9 · 💀) - 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 (🥉5 · ⭐ 2 · 💀) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3
- glp (🥉4 · ⭐ 17) - tools for graph-based machine-learning potentials in jax. MIT
- NequIP-JAX (🥉4 · ⭐ 16 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed
- TensorPotential (🥉4 · ⭐ 7 · 💤) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom
- ACE Workflows (🥉4 · 💤) - Workflow Examples for ACE Models. Unlicensed Julia workflows
- PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
- MEGNetSparse (🥉3 · ⭐ 1 · 💤) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
- SingleNN (🥉2 · ⭐ 8 · 💀) - An efficient package for training and executing neural-network interatomic potentials. Unlicensed C++
- RuNNer (🥉2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran
- mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia
## Language Models
_Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML._
paper-qa (🥇26 · ⭐ 3.8K) - LLM Chain for answering questions from documents with citations. Apache-2
- [GitHub](https://github.com/whitead/paper-qa) (👨💻 17 · 🔀 350 · 📦 64 · 📋 140 - 48% open · ⏱️ 28.06.2024):
```
git clone https://github.com/whitead/paper-qa
```
- [PyPi](https://pypi.org/project/paper-qa) (📥 5K / month):
```
pip install paper-qa
```
ChemCrow (🥇16 · ⭐ 520) - Chemcrow. MIT
- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨💻 3 · 🔀 70 · 📦 4 · 📋 16 - 12% open · ⏱️ 27.03.2024):
```
git clone https://github.com/ur-whitelab/chemcrow-public
```
- [PyPi](https://pypi.org/project/chemcrow) (📥 500 / month):
```
pip install chemcrow
```
ChemNLP project (🥈14 · ⭐ 140) - ChemNLP project. MIT datasets
- [GitHub](https://github.com/OpenBioML/chemnlp) (👨💻 26 · 🔀 45 · 📋 250 - 44% open · ⏱️ 01.04.2024):
```
git clone https://github.com/OpenBioML/chemnlp
```
- [PyPi](https://pypi.org/project/chemnlp) (📥 38 / month):
```
pip install chemnlp
```
gptchem (🥈13 · ⭐ 220 · 💤) - Use GPT-3 to solve chemistry problems. MIT
- [GitHub](https://github.com/kjappelbaum/gptchem) (👨💻 4 · 🔀 39 · 📋 21 - 90% open · ⏱️ 04.10.2023):
```
git clone https://github.com/kjappelbaum/gptchem
```
- [PyPi](https://pypi.org/project/gptchem) (📥 59 / month):
```
pip install gptchem
```
LLaMP (🥈12 · ⭐ 47) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD language-models Python general-tool
- [GitHub](https://github.com/chiang-yuan/llamp) (👨💻 5 · 🔀 4 · 📋 25 - 32% open · ⏱️ 03.07.2024):
```
git clone https://github.com/chiang-yuan/llamp
```
MoLFormer (🥉9 · ⭐ 220 · 💤) - Repository for MolFormer. Apache-2 transformer pre-trained drug-discovery
- [GitHub](https://github.com/IBM/molformer) (👨💻 5 · 🔀 40 · 📋 18 - 44% open · ⏱️ 16.10.2023):
```
git clone https://github.com/IBM/molformer
```
MolSkill (🥉9 · ⭐ 100 · 💤) - Extracting medicinal chemistry intuition via preference machine learning. MIT drug-discovery recommender
- [GitHub](https://github.com/microsoft/molskill) (👨💻 4 · 🔀 8 · 📋 5 - 40% open · ⏱️ 31.10.2023):
```
git clone https://github.com/microsoft/molskill
```
- [Conda](https://anaconda.org/msr-ai4science/molskill) (📥 260 · ⏱️ 18.06.2023):
```
conda install -c msr-ai4science molskill
```
chemlift (🥉7 · ⭐ 31 · 💤) - Language-interfaced fine-tuning for chemistry. MIT
- [GitHub](https://github.com/lamalab-org/chemlift) (👨💻 2 · 🔀 3 · 📋 18 - 61% open · ⏱️ 14.10.2023):
```
git clone https://github.com/lamalab-org/chemlift
```
SciBot (🥉6 · ⭐ 28) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed
- [GitHub](https://github.com/CFN-softbio/SciBot) (🔀 8 · 📦 1 · ⏱️ 19.04.2024):
```
git clone https://github.com/CFN-softbio/SciBot
```
LLM-Prop (🥉6 · ⭐ 24) - A repository for the LLM-Prop implementation. MIT
- [GitHub](https://github.com/vertaix/LLM-Prop) (👨💻 6 · 🔀 5 · 📋 2 - 50% open · ⏱️ 26.04.2024):
```
git clone https://github.com/vertaix/LLM-Prop
```
BERT-PSIE-TC (🥉5 · ⭐ 10 · 💤) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism
- [GitHub](https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC) (👨💻 2 · 🔀 3 · ⏱️ 18.08.2023):
```
git clone https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC
```
MAPI_LLM (🥉5 · ⭐ 8) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT dataset
- [GitHub](https://github.com/maykcaldas/MAPI_LLM) (👨💻 2 · 🔀 2 · ⏱️ 11.04.2024):
```
git clone https://github.com/maykcaldas/MAPI_LLM
```
Show 5 hidden projects...
- ChemDataExtractor (🥇16 · ⭐ 290 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
- mat2vec (🥈12 · ⭐ 620 · 💀) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn
- nlcc (🥉11 · ⭐ 43 · 💀) - Natural language computational chemistry command line interface. MIT single-paper
- ChemDataWriter (🥉4 · ⭐ 14 · 💤) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data
- CatBERTa (🥉3 · ⭐ 19) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis
## Materials Discovery
_Projects that implement materials discovery methods using atomistic ML._
aviary (🥇15 · ⭐ 45) - The Wren sits on its Roost in the Aviary. MIT
- [GitHub](https://github.com/CompRhys/aviary) (👨💻 4 · 🔀 10 · 📋 26 - 7% open · ⏱️ 22.07.2024):
```
git clone https://github.com/CompRhys/aviary
```
Materials Discovery: GNoME (🥈10 · ⭐ 840 · 💤) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. Apache-2 rep-learn datasets
- [GitHub](https://github.com/google-deepmind/materials_discovery) (👨💻 2 · 🔀 130 · 📋 20 - 80% open · ⏱️ 02.12.2023):
```
git clone https://github.com/google-deepmind/materials_discovery
```
CSPML (crystal structure prediction with machine learning-based element substitution) (🥉5 · ⭐ 16) - Original implementation of CSPML. Unlicensed structure-prediction
- [GitHub](https://github.com/Minoru938/CSPML) (🔀 8 · 📋 3 - 66% open · ⏱️ 09.07.2024):
```
git clone https://github.com/minoru938/cspml
```
Show 6 hidden projects...
- BOSS (🥈7 · ⭐ 20 · 💀) - Bayesian Optimization Structure Search (BOSS). Unlicensed probabilistic
- AGOX (🥈6 · ⭐ 13 · 💀) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization
- Computational Autonomy for Materials Discovery (CAMD) (🥈6 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2
- SPINNER (🥉4 · ⭐ 11 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction
- closed-loop-acceleration-benchmarks (🥉4 · 💀) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
- sl_discovery (🥉3 · ⭐ 5 · 💀) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper
## Mathematical tools
_Projects that implement mathematical objects used in atomistic machine learning._
KFAC-JAX (🥇19 · ⭐ 210) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2
- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨💻 13 · 🔀 16 · 📦 10 · 📋 13 - 30% open · ⏱️ 21.07.2024):
```
git clone https://github.com/deepmind/kfac-jax
```
- [PyPi](https://pypi.org/project/kfac-jax) (📥 920 / month):
```
pip install kfac-jax
```
gpax (🥇17 · ⭐ 190) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning
- [GitHub](https://github.com/ziatdinovmax/gpax) (👨💻 6 · 🔀 23 · 📦 1 · 📋 39 - 17% open · ⏱️ 21.05.2024):
```
git clone https://github.com/ziatdinovmax/gpax
```
- [PyPi](https://pypi.org/project/gpax) (📥 230 / month):
```
pip install gpax
```
SpheriCart (🥈16 · ⭐ 59) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT
- [GitHub](https://github.com/lab-cosmo/sphericart) (👨💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 28 - 53% open · ⏱️ 17.07.2024):
```
git clone https://github.com/lab-cosmo/sphericart
```
- [PyPi](https://pypi.org/project/sphericart) (📥 150 / month):
```
pip install sphericart
```
Polynomials4ML.jl (🥈15 · ⭐ 12) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia
- [GitHub](https://github.com/ACEsuit/Polynomials4ML.jl) (👨💻 10 · 🔀 5 · 📋 51 - 33% open · ⏱️ 22.06.2024):
```
git clone https://github.com/ACEsuit/Polynomials4ML.jl
```
GElib (🥈9 · ⭐ 19) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0 C++
- [GitHub](https://github.com/risi-kondor/GElib) (👨💻 4 · 🔀 3 · 📋 5 - 40% open · ⏱️ 26.04.2024):
```
git clone https://github.com/risi-kondor/GElib
```
COSMO Toolbox (🥉6 · ⭐ 7) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++
- [GitHub](https://github.com/lab-cosmo/toolbox) (👨💻 9 · 🔀 5 · ⏱️ 19.03.2024):
```
git clone https://github.com/lab-cosmo/toolbox
```
Show 5 hidden projects...
- lie-nn (🥈9 · ⭐ 26 · 💀) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn
- cnine (🥉6 · ⭐ 4) - Cnine tensor library. Unlicensed C++
- EquivariantOperators.jl (🥉5 · ⭐ 18 · 💤) - MIT Julia
- torch_spex (🥉3 · ⭐ 3) - Spherical expansions in PyTorch. Unlicensed
- Wigner Kernels (🥉2 · ⭐ 1 · 💤) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking
## Molecular Dynamics
_Projects that simplify the integration of molecular dynamics and atomistic machine learning._
JAX-MD (🥇24 · ⭐ 1.1K) - Differentiable, Hardware Accelerated, Molecular Dynamics. Apache-2
- [GitHub](https://github.com/jax-md/jax-md) (👨💻 32 · 🔀 180 · 📦 53 · 📋 150 - 46% open · ⏱️ 19.07.2024):
```
git clone https://github.com/jax-md/jax-md
```
- [PyPi](https://pypi.org/project/jax-md) (📥 2.8K / month):
```
pip install jax-md
```
mlcolvar (🥈19 · ⭐ 89 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT enhanced-sampling
- [GitHub](https://github.com/luigibonati/mlcolvar) (👨💻 8 · 🔀 23 · 📦 2 · 📋 70 - 18% open · ⏱️ 14.06.2024):
```
git clone https://github.com/luigibonati/mlcolvar
```
- [PyPi](https://pypi.org/project/mlcolvar) (📥 160 / month):
```
pip install mlcolvar
```
openmm-torch (🥈17 · ⭐ 180) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++
- [GitHub](https://github.com/openmm/openmm-torch) (👨💻 8 · 🔀 24 · 📋 89 - 26% open · ⏱️ 09.07.2024):
```
git clone https://github.com/openmm/openmm-torch
```
- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 370K · ⏱️ 03.06.2024):
```
conda install -c conda-forge openmm-torch
```
FitSNAP (🥈17 · ⭐ 140) - Software for generating SNAP machine-learning interatomic potentials. GPL-2.0
- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨💻 24 · 🔀 47 · 📥 8 · 📦 2 · 📋 66 - 15% open · ⏱️ 27.06.2024):
```
git clone https://github.com/FitSNAP/FitSNAP
```
- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 7.2K · ⏱️ 16.06.2023):
```
conda install -c conda-forge fitsnap3
```
OpenMM-ML (🥉14 · ⭐ 75) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP
- [GitHub](https://github.com/openmm/openmm-ml) (👨💻 5 · 🔀 19 · 📋 52 - 40% open · ⏱️ 06.06.2024):
```
git clone https://github.com/openmm/openmm-ml
```
- [Conda](https://anaconda.org/conda-forge/openmm-ml) (📥 4.2K · ⏱️ 07.06.2024):
```
conda install -c conda-forge openmm-ml
```
pair_nequip (🥉11 · ⭐ 37) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn
- [GitHub](https://github.com/mir-group/pair_nequip) (👨💻 3 · 🔀 13 · 📋 28 - 28% open · ⏱️ 05.06.2024):
```
git clone https://github.com/mir-group/pair_nequip
```
pair_allegro (🥉9 · ⭐ 34) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn
- [GitHub](https://github.com/mir-group/pair_allegro) (👨💻 2 · 🔀 7 · 📋 26 - 30% open · ⏱️ 05.06.2024):
```
git clone https://github.com/mir-group/pair_allegro
```
PACE (🥉9 · ⭐ 21 · 💤) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom
- [GitHub](https://github.com/ICAMS/lammps-user-pace) (👨💻 6 · 🔀 10 · ⏱️ 27.11.2023):
```
git clone https://github.com/ICAMS/lammps-user-pace
```
SOMD (🥉5 · ⭐ 11 · 📉) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning
- [GitHub](https://github.com/initqp/somd) (🔀 2 · ⏱️ 03.07.2024):
```
git clone https://github.com/initqp/somd
```
Show 1 hidden projects...
- interface-lammps-mlip-3 (🥉3 · ⭐ 5 · 💀) - An interface between LAMMPS and MLIP (version 3). GPL-2.0
## Reinforcement Learning
_Projects that focus on reinforcement learning for atomistic ML._
Show 2 hidden projects...
- ReLeaSE (🥇11 · ⭐ 340 · 💀) - Deep Reinforcement Learning for de-novo Drug Design. MIT drug-discovery
- CatGym (🥉6 · ⭐ 11 · 💀) - Surface segregation using Deep Reinforcement Learning. GPL
## Representation Engineering
_Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering._
cdk (🥇24 · ⭐ 480) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
- [GitHub](https://github.com/cdk/cdk) (👨💻 160 · 🔀 150 · 📥 21K · 📋 280 - 10% open · ⏱️ 16.07.2024):
```
git clone https://github.com/cdk/cdk
```
- [Maven](https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle):
```
org.openscience.cdk
cdk-bundle
[VERSION]
```
DScribe (🥇22 · ⭐ 390) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2
- [GitHub](https://github.com/SINGROUP/dscribe) (👨💻 18 · 🔀 86 · 📦 190 · 📋 98 - 7% open · ⏱️ 28.05.2024):
```
git clone https://github.com/SINGROUP/dscribe
```
- [PyPi](https://pypi.org/project/dscribe) (📥 360K / month):
```
pip install dscribe
```
- [Conda](https://anaconda.org/conda-forge/dscribe) (📥 110K · ⏱️ 28.05.2024):
```
conda install -c conda-forge dscribe
```
MODNet (🥈16 · ⭐ 72) - MODNet: a framework for machine learning materials properties. MIT pre-trained small-data transfer-learning
- [GitHub](https://github.com/ppdebreuck/modnet) (👨💻 8 · 🔀 31 · 📦 6 · 📋 42 - 35% open · ⏱️ 24.06.2024):
```
git clone https://github.com/ppdebreuck/modnet
```
GlassPy (🥈14 · ⭐ 26) - Python module for scientists working with glass materials. GPL-3.0
- [GitHub](https://github.com/drcassar/glasspy) (🔀 7 · 📦 5 · 📋 6 - 16% open · ⏱️ 21.01.2024):
```
git clone https://github.com/drcassar/glasspy
```
- [PyPi](https://pypi.org/project/glasspy) (📥 160 / month):
```
pip install glasspy
```
SISSO (🥈13 · ⭐ 230 · 💤) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran
- [GitHub](https://github.com/rouyang2017/SISSO) (👨💻 3 · 🔀 76 · 📋 62 - 8% open · ⏱️ 12.09.2023):
```
git clone https://github.com/rouyang2017/SISSO
```
Librascal (🥈13 · ⭐ 79 · 💤) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1
- [GitHub](https://github.com/lab-cosmo/librascal) (👨💻 30 · 🔀 19 · 📋 230 - 43% open · ⏱️ 30.11.2023):
```
git clone https://github.com/lab-cosmo/librascal
```
Rascaline (🥈12 · ⭐ 44) - Computing representations for atomistic machine learning. BSD-3 Rust C++
- [GitHub](https://github.com/Luthaf/rascaline) (👨💻 14 · 🔀 13 · 📋 65 - 47% open · ⏱️ 02.07.2024):
```
git clone https://github.com/Luthaf/rascaline
```
NICE (🥉7 · ⭐ 12) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT
- [GitHub](https://github.com/lab-cosmo/nice) (👨💻 4 · 🔀 3 · 📋 3 - 66% open · ⏱️ 15.04.2024):
```
git clone https://github.com/lab-cosmo/nice
```
Show 15 hidden projects...
- CatLearn (🥇17 · ⭐ 98 · 💀) - GPL-3.0 surface-science
- cmlkit (🥈10 · ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking
- CBFV (🥉9 · ⭐ 21 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
- BenchML (🥉9 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking
- SkipAtom (🥉7 · ⭐ 23 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
- milad (🥉6 · ⭐ 29 · 💀) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative
- SA-GPR (🥉6 · ⭐ 18 · 💀) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang
- fplib (🥉6 · ⭐ 7 · 💀) - a fingerprint library. MIT C-lang single-paper
- SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
- soap_turbo (🥉6 · ⭐ 4 · 💀) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
- pyLODE (🥉6 · ⭐ 2 · 💤) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
- SISSO++ (🥉5 · ⭐ 3 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++
- magnetism-prediction (🥉4 · ⭐ 1 · 💤) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper
- ML-for-CurieTemp-Predictions (🥉3 · ⭐ 1 · 💀) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
- AMP (🥉2) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed
## Representation Learning
_General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN)._
Deep Graph Library (DGL) (🥇38 · ⭐ 13K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2
- [GitHub](https://github.com/dmlc/dgl) (👨💻 290 · 🔀 2.9K · 📦 270 · 📋 2.7K - 14% open · ⏱️ 23.07.2024):
```
git clone https://github.com/dmlc/dgl
```
- [PyPi](https://pypi.org/project/dgl) (📥 92K / month):
```
pip install dgl
```
- [Conda](https://anaconda.org/dglteam/dgl) (📥 360K · ⏱️ 28.06.2024):
```
conda install -c dglteam dgl
```
PyG Models (🥇35 · ⭐ 21K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml
- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨💻 500 · 🔀 3.5K · 📦 6.1K · 📋 3.5K - 24% open · ⏱️ 21.07.2024):
```
git clone https://github.com/pyg-team/pytorch_geometric
```
e3nn (🥇27 · ⭐ 910 · 📈) - A modular framework for neural networks with Euclidean symmetry. MIT
- [GitHub](https://github.com/e3nn/e3nn) (👨💻 31 · 🔀 130 · 📦 270 · 📋 150 - 11% open · ⏱️ 18.07.2024):
```
git clone https://github.com/e3nn/e3nn
```
- [PyPi](https://pypi.org/project/e3nn) (📥 480K / month):
```
pip install e3nn
```
- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 19K · ⏱️ 18.06.2023):
```
conda install -c conda-forge e3nn
```
SchNetPack (🥇26 · ⭐ 750) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT
- [GitHub](https://github.com/atomistic-machine-learning/schnetpack) (👨💻 35 · 🔀 200 · 📦 83 · 📋 240 - 0% open · ⏱️ 22.07.2024):
```
git clone https://github.com/atomistic-machine-learning/schnetpack
```
- [PyPi](https://pypi.org/project/schnetpack) (📥 940 / month):
```
pip install schnetpack
```
MatGL (Materials Graph Library) (🥇22 · ⭐ 240) - Graph deep learning library for materials. BSD-3
- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨💻 17 · 🔀 58 · 📦 42 · 📋 81 - 2% open · ⏱️ 21.07.2024):
```
git clone https://github.com/materialsvirtuallab/matgl
```
- [PyPi](https://pypi.org/project/m3gnet) (📥 670 / month):
```
pip install m3gnet
```
NVIDIA Deep Learning Examples for Tensor Cores (🥈21 · ⭐ 13K) - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and.. Custom educational drug-discovery
- [GitHub](https://github.com/NVIDIA/DeepLearningExamples) (👨💻 120 · 🔀 3K · 📋 830 - 30% open · ⏱️ 04.04.2024):
```
git clone https://github.com/NVIDIA/DeepLearningExamples
```
DIG: Dive into Graphs (🥈21 · ⭐ 1.8K) - A library for graph deep learning research. GPL-3.0
- [GitHub](https://github.com/divelab/DIG) (👨💻 50 · 🔀 280 · 📋 210 - 14% open · ⏱️ 04.02.2024):
```
git clone https://github.com/divelab/DIG
```
- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 610 / month):
```
pip install dive-into-graphs
```
ALIGNN (🥈20 · ⭐ 200) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom
- [GitHub](https://github.com/usnistgov/alignn) (👨💻 7 · 🔀 77 · 📦 12 · 📋 54 - 55% open · ⏱️ 15.07.2024):
```
git clone https://github.com/usnistgov/alignn
```
- [PyPi](https://pypi.org/project/alignn) (📥 960 / month):
```
pip install alignn
```
kgcnn (🥈20 · ⭐ 100) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT
- [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨💻 7 · 🔀 28 · 📦 18 · 📋 86 - 13% open · ⏱️ 06.05.2024):
```
git clone https://github.com/aimat-lab/gcnn_keras
```
- [PyPi](https://pypi.org/project/kgcnn) (📥 300 / month):
```
pip install kgcnn
```
Uni-Mol (🥈18 · ⭐ 620) - Official Repository for the Uni-Mol Series Methods. MIT pre-trained
- [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨💻 16 · 🔀 110 · 📥 14K · 📋 140 - 40% open · ⏱️ 18.07.2024):
```
git clone https://github.com/dptech-corp/Uni-Mol
```
e3nn-jax (🥈17 · ⭐ 170) - jax library for E3 Equivariant Neural Networks. Apache-2
- [GitHub](https://github.com/e3nn/e3nn-jax) (👨💻 7 · 🔀 18 · 📦 36 · ⏱️ 24.06.2024):
```
git clone https://github.com/e3nn/e3nn-jax
```
- [PyPi](https://pypi.org/project/e3nn-jax) (📥 2K / month):
```
pip install e3nn-jax
```
matsciml (🥈17 · ⭐ 130) - 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) (👨💻 11 · 🔀 17 · 📋 50 - 30% open · ⏱️ 19.07.2024):
```
git clone https://github.com/IntelLabs/matsciml
```
escnn (🥈14 · ⭐ 340 · 💤) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
- [GitHub](https://github.com/QUVA-Lab/escnn) (👨💻 10 · 🔀 42 · 📋 62 - 41% open · ⏱️ 17.10.2023):
```
git clone https://github.com/QUVA-Lab/escnn
```
- [PyPi](https://pypi.org/project/escnn) (📥 360 / month):
```
pip install escnn
```
hippynn (🥈11 · ⭐ 63) - python library for atomistic machine learning. Custom workflows
- [GitHub](https://github.com/lanl/hippynn) (👨💻 13 · 🔀 23 · 📦 1 · 📋 14 - 42% open · ⏱️ 17.07.2024):
```
git clone https://github.com/lanl/hippynn
```
Equiformer (🥈9 · ⭐ 180) - [ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer
- [GitHub](https://github.com/atomicarchitects/equiformer) (👨💻 2 · 🔀 35 · 📋 12 - 41% open · ⏱️ 18.07.2024):
```
git clone https://github.com/atomicarchitects/equiformer
```
graphite (🥈9 · ⭐ 52) - A repository for implementing graph network models based on atomic structures. MIT
- [GitHub](https://github.com/LLNL/graphite) (👨💻 2 · 🔀 9 · 📦 11 · 📋 3 - 66% open · ⏱️ 13.05.2024):
```
git clone https://github.com/llnl/graphite
```
ai4material_design (🥈9 · ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
- [GitHub](https://github.com/HSE-LAMBDA/ai4material_design) (👨💻 11 · 🔀 3 · ⏱️ 21.11.2023):
```
git clone https://github.com/HSE-LAMBDA/ai4material_design
```
EquiformerV2 (🥉8 · ⭐ 170) - [ICLR24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT
- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨💻 2 · 🔀 23 · 📋 13 - 92% open · ⏱️ 16.07.2024):
```
git clone https://github.com/atomicarchitects/equiformer_v2
```
DeeperGATGNN (🥉8 · ⭐ 44) - Scalable graph neural networks for materials property prediction. MIT
- [GitHub](https://github.com/usccolumbia/deeperGATGNN) (👨💻 3 · 🔀 8 · 📋 8 - 12% open · ⏱️ 19.01.2024):
```
git clone https://github.com/usccolumbia/deeperGATGNN
```
AdsorbML (🥉8 · ⭐ 33 · 💤) - MIT surface-science single-paper
- [GitHub](https://github.com/Open-Catalyst-Project/AdsorbML) (👨💻 5 · 🔀 4 · 📋 4 - 75% open · ⏱️ 31.07.2023):
```
git clone https://github.com/Open-Catalyst-Project/AdsorbML
```
ML4pXRDs (🥉7 · 💤) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper
- [GitHub](https://github.com/aimat-lab/ML4pXRDs) (📥 2 · ⏱️ 14.07.2023):
```
git clone https://github.com/aimat-lab/ML4pXRDs
```
Show 33 hidden projects...
- dgl-lifesci (🥇23 · ⭐ 700 · 💀) - Python package for graph neural networks in chemistry and biology. Apache-2
- benchmarking-gnns (🥈14 · ⭐ 2.5K · 💀) - Repository for benchmarking graph neural networks. MIT single-paper benchmarking
- Crystal Graph Convolutional Neural Networks (CGCNN) (🥈12 · ⭐ 620 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT
- Neural fingerprint (nfp) (🥈12 · ⭐ 57 · 💀) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom
- Compositionally-Restricted Attention-Based Network (CrabNet) (🥈12 · ⭐ 12 · 💀) - Predict materials properties using only the composition information!. MIT
- GDC (🥈10 · ⭐ 260 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative
- SE(3)-Transformers (🥈9 · ⭐ 480 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer
- molecularGNN_smiles (🥈9 · ⭐ 280 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
- GATGNN: Global Attention Graph Neural Network (🥈9 · ⭐ 66 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT
- CGAT (🥉8 · ⭐ 25 · 💀) - Crystal graph attention neural networks for materials prediction. MIT
- FAENet (🥉8 · ⭐ 25 · 💤) - MIT
- UVVisML (🥉8 · ⭐ 20 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
- T-e3nn (🥉8 · ⭐ 8 · 💀) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
- DTNN (🥉7 · ⭐ 76 · 💀) - Deep Tensor Neural Network. MIT
- Cormorant (🥉7 · ⭐ 59 · 💀) - Codebase for Cormorant Neural Networks. Custom
- escnn_jax (🥉7 · ⭐ 25 · 💀) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
- tensorfieldnetworks (🥉6 · ⭐ 150 · 💀) - MIT
- MACE-Layer (🥉6 · ⭐ 32 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT
- charge_transfer_nnp (🥉6 · ⭐ 28 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
- GLAMOUR (🥉6 · ⭐ 18 · 💀) - Graph Learning over Macromolecule Representations. MIT single-paper
- Autobahn (🥉5 · ⭐ 30 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
- SCFNN (🥉5 · ⭐ 15 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
- CraTENet (🥉5 · ⭐ 12 · 💀) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
- Per-Site CGCNN (🥉5 · ⭐ 1 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT pre-trained single-paper
- Per-site PAiNN (🥉5 · ⭐ 1 · 💀) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pre-trained single-paper
- Atom2Vec (🥉4 · ⭐ 31) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
- FieldSchNet (🥉4 · ⭐ 16 · 💀) - MIT
- Graph Transport Network (🥉4 · ⭐ 16 · 💀) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena
- gkx: Green-Kubo Method in JAX (🥉4 · ⭐ 3) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena
- atom_by_atom (🥉3 · ⭐ 6 · 💤) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper
- Element encoder (🥉3 · ⭐ 6 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
- Point Edge Transformer (🥉2) - Smooth, exact rotational symmetrization for deep learning on point clouds. CC-BY-4.0
- SphericalNet (🥉1 · ⭐ 3 · 💀) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in.. Unlicensed
## Unsupervised Learning
_Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization._
DADApy (🥇18 · ⭐ 100) - Distance-based Analysis of DAta-manifolds in python. Apache-2
- [GitHub](https://github.com/sissa-data-science/DADApy) (👨💻 19 · 🔀 16 · 📦 7 · 📋 31 - 16% open · ⏱️ 18.07.2024):
```
git clone https://github.com/sissa-data-science/DADApy
```
- [PyPi](https://pypi.org/project/dadapy) (📥 260 / month):
```
pip install dadapy
```
ASAP (🥈12 · ⭐ 140) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT
- [GitHub](https://github.com/BingqingCheng/ASAP) (👨💻 6 · 🔀 28 · 📦 6 · 📋 25 - 24% open · ⏱️ 27.06.2024):
```
git clone https://github.com/BingqingCheng/ASAP
```
Show 5 hidden projects...
- Sketchmap (🥈8 · ⭐ 44 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++
- Coarse-Graining-Auto-encoders (🥉4 · ⭐ 21 · 💀) - Unlicensed single-paper
- paper-ml-robustness-material-property (🥉4 · ⭐ 4 · 💀) - BSD-3 datasets single-paper
- KmdPlus (🥉2 · ⭐ 3 · 💤) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. Unlicensed
- Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( ⭐ 2) - Provides a workflow to obtain clustering of local environments in dataset of structures. Unlicensed
## Visualization
_Projects that focus on visualization (viz.) for atomistic ML._
pymatviz (🥇20 · ⭐ 140) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
- [GitHub](https://github.com/janosh/pymatviz) (👨💻 7 · 🔀 12 · 📦 8 · 📋 40 - 22% open · ⏱️ 22.07.2024):
```
git clone https://github.com/janosh/pymatviz
```
- [PyPi](https://pypi.org/project/pymatviz) (📥 2.6K / month):
```
pip install pymatviz
```
Chemiscope (🥉19 · ⭐ 120 · 📈) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript
- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨💻 22 · 🔀 29 · 📥 240 · 📦 6 · 📋 120 - 28% open · ⏱️ 18.07.2024):
```
git clone https://github.com/lab-cosmo/chemiscope
```
- [npm](https://www.npmjs.com/package/chemiscope) (📥 18 / month):
```
npm install chemiscope
```
ZnDraw (🥉17 · ⭐ 27) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript
- [GitHub](https://github.com/zincware/ZnDraw) (👨💻 7 · 🔀 1 · 📦 3 · 📋 250 - 24% open · ⏱️ 23.07.2024):
```
git clone https://github.com/zincware/ZnDraw
```
- [PyPi](https://pypi.org/project/zndraw) (📥 770 / month):
```
pip install zndraw
```
## 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._
DeepQMC (🥇17 · ⭐ 340) - Deep learning quantum Monte Carlo for electrons in real space. MIT
- [GitHub](https://github.com/deepqmc/deepqmc) (👨💻 13 · 🔀 60 · 📦 2 · 📋 42 - 11% open · ⏱️ 23.02.2024):
```
git clone https://github.com/deepqmc/deepqmc
```
- [PyPi](https://pypi.org/project/deepqmc) (📥 93 / month):
```
pip install deepqmc
```
FermiNet (🥈14 · ⭐ 660) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer
- [GitHub](https://github.com/google-deepmind/ferminet) (👨💻 18 · 🔀 110 · 📋 45 - 2% open · ⏱️ 04.06.2024):
```
git clone https://github.com/deepmind/ferminet
```
DeepErwin (🥉11 · ⭐ 46) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom
- [GitHub](https://github.com/mdsunivie/deeperwin) (👨💻 7 · 🔀 6 · 📥 7 · ⏱️ 07.06.2024):
```
git clone https://github.com/mdsunivie/deeperwin
```
- [PyPi](https://pypi.org/project/deeperwin) (📥 35 / month):
```
pip install deeperwin
```
Show 1 hidden projects...
- SchNOrb (🥉5 · ⭐ 58 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT
## Others
pretrained-gnns (🥇10 · ⭐ 950 · 💤) - Strategies for Pre-training Graph Neural Networks. MIT pre-trained
- [GitHub](https://github.com/snap-stanford/pretrain-gnns) (👨💻 2 · 🔀 160 · 📋 63 - 53% open · ⏱️ 29.07.2023):
```
git clone https://github.com/snap-stanford/pretrain-gnns
```
Show 1 hidden projects...
---
## 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](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose) and fill in the requested information.
- Modify the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/main/projects.yaml) with your additions or changes, and submit a pull request. This can also be done directly via the [Github UI](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/projects.yaml).
If you like to contribute to or share suggestions regarding the project metadata collection or markdown generation, please refer to the [best-of-generator](https://github.com/best-of-lists/best-of-generator) repository. If you like to create your own best-of list, we recommend to follow [this guide](https://github.com/best-of-lists/best-of/blob/main/create-best-of-list.md).
For more information on how to add or update projects, please read the [contribution guidelines](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/main/CONTRIBUTING.md). By participating in this project, you agree to abide by its [Code of Conduct](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/blob/main/.github/CODE_OF_CONDUCT.md).
## License
[](https://creativecommons.org/licenses/by-sa/4.0/)