🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.

License: Creative Commons Attribution Share Alike 4.0 International

atomistic-machine-learning awesome-list best-of-list computational-chemistry computational-materials-science condensed-matter density-functional-theory drug-discovery electronic-structure interatomic-potentials

best-of-atomistic-machine-learning's Introduction

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, 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!

🧙‍♂️ Discover other best-of lists or create your own.

Active learning 5 projects
Biomolecules 2 projects
Community resources 21 projects
Datasets 35 projects
Data Structures 4 projects
Density functional theory (ML-DFT) 26 projects
Educational Resources 24 projects
Explainable Artificial intelligence (XAI) 4 projects
Electronic structure methods (ML-ESM) 3 projects
General Tools 22 projects
Generative Models 11 projects
Interatomic Potentials (ML-IAP) 65 projects
Language Models 17 projects
Materials Discovery 9 projects
Mathematical tools 11 projects
Molecular Dynamics 10 projects
Reinforcement Learning 2 projects
Representation Engineering 23 projects
Representation Learning 54 projects
Unsupervised Learning 7 projects
Visualization 3 projects
Wavefunction methods (ML-WFT) 4 projects
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 (👨‍💻 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 (👨‍💻 8 · 🔀 7 · 📦 2 · 📋 120 - 50% open · ⏱️ 16.07.2024):
```
 git clone https://github.com/zincware/IPSuite
```
PyPi (📥 66 / month):
```
 pip install ipsuite
```

Finetuna (🥉10 · ⭐ 42) - Active Learning for Machine Learning Potentials. MIT

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 25 · 🔀 41 · 📦 15 · 📋 57 - 54% open · ⏱️ 20.01.2024):
```
 git clone https://github.com/materialsproject/matbench
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 7 · 🔀 10 · 📦 2 · 📋 33 - 6% open · ⏱️ 22.07.2024):
```
 git clone https://github.com/janosh/matbench-discovery
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 28 · 🔀 41 · 📦 42 · 📋 440 - 20% open · ⏱️ 22.07.2024):
```
 git clone https://github.com/Materials-Consortia/optimade-python-tools
```
PyPi (📥 8.5K / month):
```
 pip install optimade
```
Conda (📥 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 (👨‍💻 25 · 🔀 19 · 📦 38 · 📋 98 - 20% open · ⏱️ 20.06.2024):
```
 git clone https://github.com/materialsproject/MPContribs
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (🔀 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 (👨‍💻 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 (👨‍💻 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 (🔀 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 (👨‍💻 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 (👨‍💻 60 · 🔀 120 · 📦 120 · 📋 94 - 18% open · ⏱️ 06.06.2024):
```
 git clone https://github.com/deepmodeling/dpdata
```
PyPi (📥 45K / month):
```
 pip install dpdata
```
Conda (📥 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 (👨‍💻 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 (👨‍💻 8 · 🔀 6 · 📦 22 · 📋 4 - 25% open · ⏱️ 23.02.2024):
```
 git clone https://github.com/materialsproject/pyrho
```
PyPi (📥 6.5K / month):
```
 pip install mp-pyrho
```

dlpack (🥉15 · ⭐ 870) - common in-memory tensor structure. Apache-2 C++

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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. 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 7 · 🔀 40 · 📦 20 · 📋 69 - 15% open · ⏱️ 04.12.2023):
```
 git clone https://github.com/ur-whitelab/exmol
```
PyPi (📥 600 / month):
```
 pip install exmol
```

MEGAN: Multi Explanation Graph Attention Student (🥈6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 250 · 🔀 1.6K · 📦 400 · 📋 1.7K - 26% open · ⏱️ 10.07.2024):
```
 git clone https://github.com/deepchem/deepchem
```
PyPi (📥 29K / month):
```
 pip install deepchem
```
Conda (📥 110K · ⏱️ 05.04.2024):
```
 conda install -c conda-forge deepchem
```
Docker Hub (📥 7.3K · ⭐ 5 · ⏱️ 10.07.2024):
```
 docker pull deepchemio/deepchem
```

RDKit (🥇32 · ⭐ 2.5K) - BSD-3 C++

GitHub (👨‍💻 230 · 🔀 830 · 📥 1.2K · 📦 3 · 📋 3.2K - 28% open · ⏱️ 23.07.2024):
```
 git clone https://github.com/rdkit/rdkit
```
PyPi (📥 640K / month):
```
 pip install rdkit
```
Conda (📥 2.6M · ⏱️ 16.06.2023):
```
 conda install -c rdkit rdkit
```

Matminer (🥇27 · ⭐ 450 · 📉) - Data mining for materials science. Custom

GitHub (👨‍💻 54 · 🔀 180 · 📦 300 · 📋 220 - 10% open · ⏱️ 27.05.2024):
```
 git clone https://github.com/hackingmaterials/matminer
```
PyPi (📥 10K / month):
```
 pip install matminer
```
Conda (📥 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 (👨‍💻 84 · 🔀 120 · 📥 510 · 📦 38 · 📋 460 - 22% open · ⏱️ 19.07.2024):
```
 git clone https://github.com/libAtoms/QUIP
```
PyPi (📥 3.1K / month):
```
 pip install quippy-ase
```
Docker Hub (📥 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 (👨‍💻 32 · 🔀 73 · 📦 9 · 📋 68 - 10% open · ⏱️ 03.07.2024):
```
 git clone https://github.com/materialsvirtuallab/maml
```
PyPi (📥 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 (👨‍💻 15 · 🔀 120 · 📦 92 · 📋 89 - 49% open · ⏱️ 28.06.2024):
```
 git clone https://github.com/usnistgov/jarvis
```
PyPi (📥 16K / month):
```
 pip install jarvis-tools
```

Conda (📥 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 (👨‍💻 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 (👨‍💻 15 · 🔀 18 · 📦 10 · 📋 69 - 18% open · ⏱️ 15.06.2024):
```
 git clone https://github.com/scikit-learn-contrib/scikit-matter
```
PyPi (📥 1.3K / month):
```
 pip install skmatter
```
Conda (📥 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 (👨‍💻 10 · 🔀 57 · 📥 1.4K · 📋 86 - 23% open · ⏱️ 21.04.2024):
```
 git clone https://github.com/yoshida-lab/XenonPy
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 20 · 🔀 66 · 📋 99 - 1% open · ⏱️ 04.07.2024):
```
 git clone https://github.com/GT4SD/gt4sd-core
```
PyPi (📥 730 / month):
```
 pip install gt4sd
```

MoLeR (🥇15 · ⭐ 250) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT

GitHub (👨‍💻 5 · 🔀 36 · 📋 37 - 24% open · ⏱️ 03.01.2024):
```
 git clone https://github.com/microsoft/molecule-generation
```
PyPi (📥 380 / month):
```
 pip install molecule-generation
```

SchNetPack G-SchNet (🥈14 · ⭐ 42) - G-SchNet extension for SchNetPack. MIT

GitHub (👨‍💻 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 (🔀 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 (👨‍💻 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 (👨‍💻 69 · 🔀 470 · 📥 37K · 📦 16 · 📋 730 - 10% open · ⏱️ 06.07.2024):
```
 git clone https://github.com/deepmodeling/deepmd-kit
```
PyPi (📥 1.8K / month):
```
 pip install deepmd-kit
```

Conda (📥 1.2K · ⏱️ 06.04.2024):

 conda install -c deepmodeling deepmd-kit

Docker Hub (📥 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 (👨‍💻 11 · 🔀 120 · 📦 19 · 📋 87 - 24% open · ⏱️ 09.07.2024):
```
 git clone https://github.com/mir-group/nequip
```
PyPi (📥 2.8K / month):
```
 pip install nequip
```
Conda (📥 5K · ⏱️ 10.07.2024):
```
 conda install -c conda-forge nequip
```

TorchANI (🥇22 · ⭐ 450 · 💤) - Accurate Neural Network Potential on PyTorch. MIT

GitHub (👨‍💻 17 · 🔀 120 · 📦 39 · 📋 170 - 13% open · ⏱️ 14.11.2023):
```
 git clone https://github.com/aiqm/torchani
```
PyPi (📥 4.8K / month):
```
 pip install torchani
```
Conda (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 16 · 🔀 69 · 📋 100 - 20% open · ⏱️ 09.07.2024):
```
 git clone https://github.com/torchmd/torchmd-net
```

Conda (📥 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 (👨‍💻 64 · 🔀 170 · 📥 1.7K · 📦 6 · 📋 290 - 10% open · ⏱️ 10.04.2024):
```
 git clone https://github.com/deepmodeling/dpgen
```
PyPi (📥 460 / month):
```
 pip install dpgen
```
Conda (📥 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 (👨‍💻 7 · 🔀 55 · 📦 25 · 📋 50 - 6% open · ⏱️ 07.07.2024):
```
 git clone https://github.com/CederGroupHub/chgnet
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 10 · 🔀 20 · 📦 1 · 📋 42 - 30% open · ⏱️ 12.07.2024):
```
 git clone https://github.com/uf3/uf3
```
PyPi (📥 29 / month):
```
 pip install uf3
```

KLIFF (🥈15 · ⭐ 34) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows

GitHub (👨‍💻 9 · 🔀 20 · 📦 3 · 📋 40 - 52% open · ⏱️ 06.07.2024):
```
 git clone https://github.com/openkim/kliff
```
PyPi (📥 60 / month):
```
 pip install kliff
```
Conda (📥 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 (👨‍💻 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 (👨‍💻 8 · 🔀 36 · 📦 9 · 📋 17 - 35% open · ⏱️ 31.08.2023):
```
 git clone https://github.com/stefanch/sGDML
```
PyPi (📥 120 / month):
```
 pip install sgdml
```

PyXtalFF (🥈14 · ⭐ 85) - Machine Learning Interatomic Potential Predictions. MIT

GitHub (👨‍💻 9 · 🔀 23 · 📋 62 - 17% open · ⏱️ 07.01.2024):
```
 git clone https://github.com/MaterSim/PyXtal_FF
```
PyPi (📥 120 / month):
```
 pip install pyxtal_ff
```

NNPOps (🥈13 · ⭐ 79) - High-performance operations for neural network potentials. MIT MD C++

GitHub (👨‍💻 9 · 🔀 17 · 📋 55 - 38% open · ⏱️ 10.07.2024):
```
 git clone https://github.com/openmm/NNPOps
```
Conda (📥 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 (👨‍💻 7 · 🔀 1 · 📦 2 · 📋 110 - 16% open · ⏱️ 17.05.2024):
```
 git clone https://github.com/apax-hub/apax
```
PyPi (📥 84 / month):
```
 pip install apax
```

CCS_fit (🥈13 · ⭐ 7) - Curvature Constrained Splines. GPL-3.0

GitHub (👨‍💻 8 · 🔀 10 · 📥 410 · 📋 14 - 57% open · ⏱️ 16.02.2024):
```
 git clone https://github.com/Teoroo-CMC/CCS
```
PyPi (📥 380 / month):
```
 pip install ccs_fit
```

ANI-1 (🥈12 · ⭐ 220) - ANI-1 neural net potential with python interface (ASE). MIT

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 5 · 🔀 15 · 📋 45 - 28% open · ⏱️ 22.07.2024):
```
 git clone https://github.com/ICAMS/python-ace
```
PyPi (📥 25 / month):
```
 pip install python-ace
```

PiNN (🥈11 · ⭐ 100) - A Python library for building atomic neural networks. BSD-3

GitHub (👨‍💻 5 · 🔀 31 · 📋 6 - 16% open · ⏱️ 27.06.2024):
```
 git clone https://github.com/Teoroo-CMC/PiNN
```
Docker Hub (📥 240 · ⏱️ 27.06.2024):
```
 docker pull teoroo/pinn
```

So3krates (MLFF) (🥈11 · ⭐ 58) - Build neural networks for machine learning force fields with JAX. MIT

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 8 · 🔀 8 · 📋 9 - 66% open · ⏱️ 09.07.2024):
```
 git clone https://github.com/mcaroba/turbogap
```

ACEfit (🥉9 · ⭐ 8) - MIT Julia

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024):
```
 git clone https://github.com/bigd4/PyNEP
```

GAP (🥉8 · ⭐ 39) - Gaussian Approximation Potential (GAP). Custom

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 11 · ⏱️ 21.08.2023):

 git clone https://github.com/ACEsuit/ACE1pack.jl

Allegro-JAX ( ⭐ 16) - JAX implementation of the Allegro interatomic potential. Unlicensed

GitHub (👨‍💻 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 (👨‍💻 17 · 🔀 350 · 📦 64 · 📋 140 - 48% open · ⏱️ 28.06.2024):
```
 git clone https://github.com/whitead/paper-qa
```
PyPi (📥 5K / month):
```
 pip install paper-qa
```

ChemCrow (🥇16 · ⭐ 520) - Chemcrow. MIT

GitHub (👨‍💻 3 · 🔀 70 · 📦 4 · 📋 16 - 12% open · ⏱️ 27.03.2024):
```
 git clone https://github.com/ur-whitelab/chemcrow-public
```
PyPi (📥 500 / month):
```
 pip install chemcrow
```

ChemNLP project (🥈14 · ⭐ 140) - ChemNLP project. MIT datasets

GitHub (👨‍💻 26 · 🔀 45 · 📋 250 - 44% open · ⏱️ 01.04.2024):
```
 git clone https://github.com/OpenBioML/chemnlp
```
PyPi (📥 38 / month):
```
 pip install chemnlp
```

gptchem (🥈13 · ⭐ 220 · 💤) - Use GPT-3 to solve chemistry problems. MIT

GitHub (👨‍💻 4 · 🔀 39 · 📋 21 - 90% open · ⏱️ 04.10.2023):
```
 git clone https://github.com/kjappelbaum/gptchem
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 4 · 🔀 8 · 📋 5 - 40% open · ⏱️ 31.10.2023):
```
 git clone https://github.com/microsoft/molskill
```

Conda (📥 260 · ⏱️ 18.06.2023):

 conda install -c msr-ai4science molskill

chemlift (🥉7 · ⭐ 31 · 💤) - Language-interfaced fine-tuning for chemistry. MIT

GitHub (👨‍💻 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 (🔀 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (🔀 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 (👨‍💻 13 · 🔀 16 · 📦 10 · 📋 13 - 30% open · ⏱️ 21.07.2024):
```
 git clone https://github.com/deepmind/kfac-jax
```
PyPi (📥 920 / month):
```
 pip install kfac-jax
```

gpax (🥇17 · ⭐ 190) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning

GitHub (👨‍💻 6 · 🔀 23 · 📦 1 · 📋 39 - 17% open · ⏱️ 21.05.2024):
```
 git clone https://github.com/ziatdinovmax/gpax
```
PyPi (📥 230 / month):
```
 pip install gpax
```

SpheriCart (🥈16 · ⭐ 59) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT

GitHub (👨‍💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 28 - 53% open · ⏱️ 17.07.2024):
```
 git clone https://github.com/lab-cosmo/sphericart
```
PyPi (📥 150 / month):
```
 pip install sphericart
```

Polynomials4ML.jl (🥈15 · ⭐ 12) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 32 · 🔀 180 · 📦 53 · 📋 150 - 46% open · ⏱️ 19.07.2024):
```
 git clone https://github.com/jax-md/jax-md
```
PyPi (📥 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 (👨‍💻 8 · 🔀 23 · 📦 2 · 📋 70 - 18% open · ⏱️ 14.06.2024):
```
 git clone https://github.com/luigibonati/mlcolvar
```
PyPi (📥 160 / month):
```
 pip install mlcolvar
```

openmm-torch (🥈17 · ⭐ 180) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++

GitHub (👨‍💻 8 · 🔀 24 · 📋 89 - 26% open · ⏱️ 09.07.2024):
```
 git clone https://github.com/openmm/openmm-torch
```

Conda (📥 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 (👨‍💻 24 · 🔀 47 · 📥 8 · 📦 2 · 📋 66 - 15% open · ⏱️ 27.06.2024):
```
 git clone https://github.com/FitSNAP/FitSNAP
```
Conda (📥 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 (👨‍💻 5 · 🔀 19 · 📋 52 - 40% open · ⏱️ 06.06.2024):
```
 git clone https://github.com/openmm/openmm-ml
```

Conda (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (🔀 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 (👨‍💻 160 · 🔀 150 · 📥 21K · 📋 280 - 10% open · ⏱️ 16.07.2024):
```
 git clone https://github.com/cdk/cdk
```

Maven:

 <dependency>
 	<groupId>org.openscience.cdk</groupId>
 	<artifactId>cdk-bundle</artifactId>
 	<version>[VERSION]</version>
 </dependency>

DScribe (🥇22 · ⭐ 390) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2

GitHub (👨‍💻 18 · 🔀 86 · 📦 190 · 📋 98 - 7% open · ⏱️ 28.05.2024):
```
 git clone https://github.com/SINGROUP/dscribe
```
PyPi (📥 360K / month):
```
 pip install dscribe
```
Conda (📥 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 (👨‍💻 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 (🔀 7 · 📦 5 · 📋 6 - 16% open · ⏱️ 21.01.2024):
```
 git clone https://github.com/drcassar/glasspy
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 290 · 🔀 2.9K · 📦 270 · 📋 2.7K - 14% open · ⏱️ 23.07.2024):
```
 git clone https://github.com/dmlc/dgl
```
PyPi (📥 92K / month):
```
 pip install dgl
```
Conda (📥 360K · ⏱️ 28.06.2024):
```
 conda install -c dglteam dgl
```

PyG Models (🥇35 · ⭐ 21K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml

GitHub (👨‍💻 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 (👨‍💻 31 · 🔀 130 · 📦 270 · 📋 150 - 11% open · ⏱️ 18.07.2024):
```
 git clone https://github.com/e3nn/e3nn
```
PyPi (📥 480K / month):
```
 pip install e3nn
```
Conda (📥 19K · ⏱️ 18.06.2023):
```
 conda install -c conda-forge e3nn
```

SchNetPack (🥇26 · ⭐ 750) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT

GitHub (👨‍💻 35 · 🔀 200 · 📦 83 · 📋 240 - 0% open · ⏱️ 22.07.2024):
```
 git clone https://github.com/atomistic-machine-learning/schnetpack
```
PyPi (📥 940 / month):
```
 pip install schnetpack
```

MatGL (Materials Graph Library) (🥇22 · ⭐ 240) - Graph deep learning library for materials. BSD-3

GitHub (👨‍💻 17 · 🔀 58 · 📦 42 · 📋 81 - 2% open · ⏱️ 21.07.2024):
```
 git clone https://github.com/materialsvirtuallab/matgl
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 50 · 🔀 280 · 📋 210 - 14% open · ⏱️ 04.02.2024):
```
 git clone https://github.com/divelab/DIG
```
PyPi (📥 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 (👨‍💻 7 · 🔀 77 · 📦 12 · 📋 54 - 55% open · ⏱️ 15.07.2024):
```
 git clone https://github.com/usnistgov/alignn
```
PyPi (📥 960 / month):
```
 pip install alignn
```

kgcnn (🥈20 · ⭐ 100) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT

GitHub (👨‍💻 7 · 🔀 28 · 📦 18 · 📋 86 - 13% open · ⏱️ 06.05.2024):
```
 git clone https://github.com/aimat-lab/gcnn_keras
```
PyPi (📥 300 / month):
```
 pip install kgcnn
```

Uni-Mol (🥈18 · ⭐ 620) - Official Repository for the Uni-Mol Series Methods. MIT pre-trained

GitHub (👨‍💻 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 (👨‍💻 7 · 🔀 18 · 📦 36 · ⏱️ 24.06.2024):
```
 git clone https://github.com/e3nn/e3nn-jax
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 10 · 🔀 42 · 📋 62 - 41% open · ⏱️ 17.10.2023):
```
 git clone https://github.com/QUVA-Lab/escnn
```
PyPi (📥 360 / month):
```
 pip install escnn
```

hippynn (🥈11 · ⭐ 63) - python library for atomistic machine learning. Custom workflows

GitHub (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 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 (👨‍💻 3 · 🔀 8 · 📋 8 - 12% open · ⏱️ 19.01.2024):
```
 git clone https://github.com/usccolumbia/deeperGATGNN
```

AdsorbML (🥉8 · ⭐ 33 · 💤) - MIT surface-science single-paper

GitHub (👨‍💻 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 (📥 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 (👨‍💻 19 · 🔀 16 · 📦 7 · 📋 31 - 16% open · ⏱️ 18.07.2024):
```
 git clone https://github.com/sissa-data-science/DADApy
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 7 · 🔀 12 · 📦 8 · 📋 40 - 22% open · ⏱️ 22.07.2024):
```
 git clone https://github.com/janosh/pymatviz
```
PyPi (📥 2.6K / month):
```
 pip install pymatviz
```

Chemiscope (🥉19 · ⭐ 120 · 📈) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript

GitHub (👨‍💻 22 · 🔀 29 · 📥 240 · 📦 6 · 📋 120 - 28% open · ⏱️ 18.07.2024):
```
 git clone https://github.com/lab-cosmo/chemiscope
```
npm (📥 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 (👨‍💻 7 · 🔀 1 · 📦 3 · 📋 250 - 24% open · ⏱️ 23.07.2024):
```
 git clone https://github.com/zincware/ZnDraw
```
PyPi (📥 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 (👨‍💻 13 · 🔀 60 · 📦 2 · 📋 42 - 11% open · ⏱️ 23.02.2024):
```
 git clone https://github.com/deepqmc/deepqmc
```
PyPi (📥 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 (👨‍💻 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 (👨‍💻 7 · 🔀 6 · 📥 7 · ⏱️ 07.06.2024):
```
 git clone https://github.com/mdsunivie/deeperwin
```
PyPi (📥 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 (👨‍💻 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 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

best-of-atomistic-machine-learning's People

Contributors

Stargazers

Watchers

best-of-atomistic-machine-learning's Issues

Add project: wfl

Project details:

Project Name: wfl
Description: Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.
Github URL: libAtoms/workflow
Category: mliap
Labels: workflows, htc
License: None
Package Managers: None

Additional context:

Original publication: https://arxiv.org/abs/2306.11421v1

Use manual description copied from README.md rather than automatic one, that one is too short.

Add project: Materials Project - Charge Densities

Project details:

Dataset.

Project Name: Materials Project - Charge Densities
resource: true
Homepage: https://materialsproject.org/ml/charge_densities
Category: datasets
License:
Package Managers:

Package.

Project Name: mp-pyrho
Github URL: https://github.com/materialsproject/pyrho
Category: data-structures or math
License: https://github.com/materialsproject/pyrho
Package Managers: pypi:mp-pyrho

Add project: EquiformerV2

Project details:

Project Name: EquiformerV2
Github URL: atomicarchitects/equiformer_v2
Category: rep-learn
License: None
Package Managers: None

Additional context:

The actual model is implemented in the ocp package, see README.

Add project: Materials repositories popular in AML research

Project details:

See below.

Additional context:

This is a tricky subject. Materials repositories. Should they be part of this list, or rather be in a separate "materials informatics" list or similar?

Yes: Other "static" datasets like QM9 are also listed here. Why? Because they are part of atomistic ML (AML) publications as much as the models on which they were trained. Same goes for publications that used materials repos like Materials Project, then.

No. This is a bigger undertaking. There are a lot of materials repositories. Which one should be added? Add only the homepagess as resources, or homepages and APIs as separate entries, or homepages and APIs and associated tools as separate entries?

A balance should be struck here. Adding all kinds of materials informatics and computational materials science tools in this AML list would dilute it. For example, pymatgen and spglib should not be listed here, but in such a dedicated list. So, strike a balance: Only add the minimal set of tools to access materials repo data via browser or via API. Two entries at most per repo as guideline.

List of projects to research

Note. Add only those that actually have a record of being used in published AML research.

Materials Project
AFLOWlib

List of projects to add to the list

Project Name: Materials Project API
Github URL: https://github.com/materialsproject/api
Category: datasets. Or create new category, see below.
License: https://github.com/materialsproject/api/blob/main/LICENSE
Package Managers: pypi:mp-api

Anonymous community feedback

Configuration Change:

Anonymous community feedback 2023-06-13.

Disable license warnings. Which licenses are risky is a matter of opinion.
Add an explanation of the "combined project-quality score".
MD category: Add LAMMPS & all the lammps-xxx repo integrating a given potential with lammps; as well as OpenMM and TorchMD, both usable with ML potentials.

Improvement suggestions to the best-of-generator template.

Allow projects in multiple categories.
- For now, the template only allows one category per project. However, one can implement this also by duplicating a project entry for different categories. That should not require a change to the template. The question is, then, where to draw the line of duplication, in order to stay fair to every package. Ultimately, this shows the deficit of the category (tree-based) vs. label (tag-based) classification schemes. Simplest would be to just do this case-by-case, whenever there is a package developer / community request for it. In that case:
- Put equisolve in multiple categories: math, mlp, general-tools. Developer request.
If the way the project score is calculated raises concern, create an issue with improvement suggestions for .

Add project: matsci.org

Project details:

Project Name: matsci.org
URL: https://matsci.org/
Description: "Computational materials science community, online forum."
Category: community-resource

Additional context:

Arguable whether this community resource should be put here, as it is more about general atomistic modeling and simulation, rather than atomistic machine learning. But on the other hand, there is no such dedicated AML community forum, so this is an okay place to put AML question. And AML is a sub- or site field of atomistic modeling will just merge into the general field as time moves on, so why even make a distinction.

Add project: SkipAtom

Project details:

Project Name: SkipAtom
Github URL: https://github.com/lantunes/skipatom
Category: representations
License: MIT
Package Managers: pypi:skipatom conda:conda-forge/skipatom

Additional context:

https://www.nature.com/articles/s41524-022-00729-3

Comparison of Word2Vec, Atom2Vec, Mat2Vec, SkipAtom.

https://blog.materialis.ai/distributed-representations-of-atoms.html

Add project: MPContribs

Project details:

Project Name: MPContribs
Github URL: https://github.com/materialsproject/MPContribs
Category: community-resource
License: MIT
Package Managers:

Rather use the resource URL.

https://docs.materialsproject.org/services/mpcontribs

Why add this project in this atomistic ML list? Because of "MPContribsML", "Benchmark datasets for Machine Learning".

Add project: TorchMD-NET

Community request, moved here from #10.

Project details:

Project Name: TorchMD-NET
Github URL: torchmd/torchmd-net
Category: md
Labels: md, mliap, rep-learn
License: MIT
Package Managers: None

Add project: CrystaLLM

Project details:

Project Name: CrystaLLM
GitHub URL: None
URL: https://crystallm.com
Category: community-service
Labels: ?
License: NA
Package Managers: None

Additional context:

https://arxiv.org/abs/2307.04340

Paper with online demo, but no code. But the online demo can be used like a prompt-based crystal structure file generator.

Add project: OpenMM ML projects

Community request, moved here from #10.

Project details:

Project Name: OpenMM-ML
Github URL: openmm/openmm-ml
Category: md
Labels: md, mliap
License: MIT
Package Managers: conda:conda-forge/openmm-ml

Project Name: SPICE
Github URL: openmm/spice-dataset
Category: datasets
Labels: datasets, mliap, md
License: MIT
Package Managers: None

Project Name: NNPOps
Github URL: openmm/NNPOps
Category: mliap
Labels: mliap, md, lang-cpp
License: MIT
Package Managers: conda:conda-forge/nnpops

Project Name: openmm-torch
Github URL: openmm/openmm-torch
Category: md
Labels: md, mliap, lang-cpp
License: https://github.com/openmm/openmm-torch#license
Package Managers: conda:conda-forge/openmm-torch

Add project: tensorfieldnetworks

Project details:

Project Name: tensorfieldnetworks
Github URL: https://github.com/tensorfieldnetworks/tensorfieldnetworks
Category: representation-learning
License: MIT
Package Managers:

Additional context:

A predecessor to e3nn. Only of historical significance, in that respect.

Add project: GT4SD

Project details:

Project Name: GT4SD
Github URL: GT4SD/gt4sd-core
Category: generative
Labels: generative, pre-trained, drug-discovery, rep-learn
License: MIT
Package Managers: pypi:gt4sd

Additional context:

Add project: EDM

Project details:

Project Name: EDM
Description: E(3) Equivariant Diffusion Model for Molecule Generation in 3D.
Github URL: ehoogeboom/e3_diffusion_for_molecules
Category: generative
Labels: generative
License: MIT
Package Managers: None

Additional context:

Original publication: https://arxiv.org/abs/2203.17003v2

Add project: Visual Graph Datasets

Project details:

Project Name: Visual Graph Datasets
Github URL: aimat-lab/visual_graph_datasets
Category: datasets
Tags: datasets, xai, rep-learn
License: MIT
Package Managers: None

Additional context:

Dataset used in original publication of #69 .

Add project: ML-for-CurieTemp-Predictions

Project details:

Project Name: ML-for-CurieTemp-Predictions
Description: Machine Learning Predictions of High-Curie-Temperature Materials
Github URL: msg-byu/ML-for-CurieTemp-Predictions
Category: rep-eng
Labels: rep-eng, single-paper, magnetism
License: MIT
Package Managers: None

Additional context:

Original publication: http://arxiv.org/abs/2307.06879.

Add project: Add all LAMMPS ML potentials

Community request, moved here from #10.

MD category: Add LAMMPS & all the lammps-xxx repo integrating a given potential with lammps; as well as OpenMM and TorchMD, both usable with ML potentials.

Subtasks OpenMM, TorchMD moved to issues #77, #78.

Update project: All where generator failed to gather license

Update details:

Project Name: All where generator failed to gather license, even though it does exist.

Manually add the project's license info in projects.yaml.

Add project: Open Catalyst datasets

Project details:

Project Name: Open Catalyst datasets
Description: The datasets of the Open Catalyst project, OC20, OC22
URL: https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md
Category: datasets
License: CC-BY-4.0
Resource: true

Additional context:

OC20, OC22

Add publication metrics

Configuration Change:

See correspondent upstream issue.

Improve labels

Configuration Change:

Right now, labels have some issues.

Problem 1). Labels with emoji name don't show up in Explanations section.

Custom labels without an image but just a name (emoji or string) are not shown in README section Explanations. That makes some emoji labels confusing. For example, ⚡ for electrostatics can only be guessed at without an "Explanation" entry.

Possible solutions.

Labels with name emoji: replace with an image emoji (an actual image file to be added to config/images). That makes text less copy & paste-able.
Replace all custom labels with name string labels. Then they are all self-explanatory.
Read template docs again and see if I have not misunderstood. Otherwise, create a template issue.

Add project: escnn

Project details:

Project Name: E(n)-equivariant Steerable CNNs (escnn)
Github URL: https://github.com/QUVA-Lab/escnn
Category: representation-learning
License: https://github.com/QUVA-Lab/escnn/blob/master/LICENSE
Package Managers: pypi:escnn

Additional context:

More general ML, computer vision. But paper also shows application example to SO(3)-equivariant molecules dataset.

Original publication: https://openreview.net/forum?id=WE4qe9xlnQw.

Add project: 2DMD dataset

Project details:

Project Name: 2DMD dataset
URL: https://rolos.com/open/2d-materials-point-defects/#physics
Category: datasets
License:

Additional context:

https://doi.org/10.1038/s41699-023-00369-1

Add project: CHGNet

Project details:

Project Name: CHGNet
Github URL: CederGroupHub/chgnet
Category: mliap
Labels: md, pre-trained, electrostatics, magnetism, structure-relaxation
License: https://github.com/CederGroupHub/chgnet/blob/main/LICENSE
Package Managers: pypi:chgnet

Additional context:

Add project: TensorMol

Project details:

Project Name: TensorMol
Github URL: jparkhill/TensorMol
Category: mliap
Labels: single-paper
License: GPL-3.0
Package Managers: None

Additional context:

Original publication https://doi.org/10.1039/C7SC04934J

Add project: MateriApps

Project details:

Project Name: MateriApps
URL: https://ma.issp.u-tokyo.ac.jp/en/
Category: Community Resources, or "Other lists" (footer.md)
License: NA?

Additional context:

What’s MateriApps

A Portal Site of Materials Science Simulation for Computational Materials Science Researchers, Theoreticians, Experimentalists, and Computer Scientists

Add project: WMDGroup's projects

Project details:

AML projects by https://wmd-group.github.io/

Add project: cdk

Project details:

Project Name: cdk
Github URL: cdk/cdk
Category: rep-eng
Labels: cheminformatics, lang-java
License: LGPL-2.1
Package Managers: pkg:maven/org.openscience.cdk/cdk-bundle

Additional context:

Add project: Artificial Intelligence for Science (AIRS)

Project details:

Project Name: Artificial Intelligence for Science (AIRS)
Github URL: https://github.com/divelab/AIRS
Category: General Tools or Representation Learning
License: GPL-3.0 license
Package Managers: None

Project Name: AI for Science Map
URL: https://www.air4.science/map
Category: Comunity resource
License: GPL-3.0 license
Description: Interactive visual treemap of the AI4Science research field, including atomistic machine learning, as of summer 2023, including papers, packages, learning resources.

TODO: add missing packages, resources from there here as well.

Project Name: AI for Science Resources
URL: https://github.com/divelab/AIRS/blob/main/Overview/resources.md
Category: Comunity resource, or add to static "similar lists" list in the README footer.
License: GPL-3.0 license
Description: List of resources for AI4Science research, including learning resources.

Add project: CraTENet

Project details:

Project Name: CraTENet
Github URL: https://github.com/lantunes/CraTENet
Category: unsure. representation-learning, but that is too unspecific. transport properties?
License: MIT
Package Managers: None

Additional context:

https://blog.materialis.ai/predicting-thermoelectric-transport-properties.html

Add project: Projects from TsudaLab

https://github.com/tsudalab

Add project: DeepCDP

Project details:

Project Name: DeepCDP: Deep learning Charge Density Prediction
Github URL: https://github.com/siddarthachar/deepcdp
Category: ml-dft
License: None
Package Managers: None

Additional context:

https://www.mdpi.com/2079-4991/13/12/1853

Add project: ai4material_design

Project details:

Project Name: ai4material_design
Github URL: https://github.com/HSE-LAMBDA/ai4material_design
Category: unsure. representation-learning or representations maybe. maybe need a new category.
License: Apache-2.0
Package Managers: None.

Additional context:

https://doi.org/10.1038/s41524-023-01062-z

Live demo.

https://research.constructor.tech/p/2d-defects-prediction

Improve labels II

Configuration Change:

Moved here from previous issue.

Problem 2). Classification by categories is not enough.

Change from categorical (one-dimensional) to tagging (multi-dimensional; here, tags are called 'labels') system. In this system, every list item has labels, and the category is only one of them, the 'main' or 'emphasized' one. Example from issue #10: Package equisolve would gain the labels math, mlp, general-tools, and the category general-tools is the category = emphasized label.

TODO:

Add labels: One for each category (duplicate structure).
Issue #10 add labels for equisolve
Issue #24 add labels for flare

Add project: PROPhet

Project details:

Project Name: PROPhet
Github URL: biklooost/PROPhet
Category: ml-dft
Labels: mliap, md, single-paper, lang-cpp
License: GPL-3.0
Package Managers: None

Additional context:

Original publication https://doi.org/10.1038/s41598-017-01251-z

Add project: PotNet

Project details:

Project Name: PotNet
Github URL: https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet
Category: rep-learn
Labels: rep-learn, mliap
Package Managers: None
Additional context:

Add project: Materials Project Charge Densities & mp-pyrho

Project details:

Project Name: Materials Project Charge Densities & mp-pyrho
Github URL: https://github.com/materialsproject/pyrho
Category: data-structures for mp-pyrho, datasets for MP Charge Densities
License: https://github.com/materialsproject/pyrho/blob/main/LICENSE
Package Managers: pypi:mp-pyrho

Additional context:

URL for the MP CD datasets project entry:

https://next-gen.materialsproject.org/ml/charge_densities

The CDs are really part of MP API now. But still worthwhile to emphasize with a separate entry that they exist in the first place.

Add project: All from Wasmer's research library

Project details:

Add all missing projects from Wasmer's research library, go to tags (lower left): library, with-code, with-data, database, dataset. For each entry, go to Attachments (upper right) > repository.

Add project: matbench, MPContribs

Project details:

Project Name: MatBench
Github URL: materialsproject/matbench
Category: community
Labels: community, datasets, benchmarking
License: MIT
Package Managers: pypi:matbench

Project Name: MPContribs
Github URL: materialsproject/MPContribs
Category: datasets
Labels: datasets
License: MIT
Package Managers: pypi:mpcontribs-client

Add project: Citrine Informatics ERD projects

Project details:

Project Name: GlassPy
Github URL: drcassar/glasspy

Category: rep-eng
Labels: rep-eng glass, composition, materials
License: GPL-3.0
Package Managers: pypi:glasspy

Are compositional feature -- based models atomistic models? In this case, I decide for "Yes".

Project Name: SciGlass
Github URL: drcassar/SciGlass

Category: datasets
Labels: datasets, glass
License: MIT

Project Name: sl_discovery
Github URL: CitrineInformatics-ERD-public/sl_discovery
Category: materials-discovery
License: Apache-2.0
Package Managers: None
Labels: materials-discovery, single-paper

Project Name: Linear vs blackbox
Github URL: CitrineInformatics-ERD-public/linear-vs-blackbox
Category: xai
Labels: xai, single-paper, rep-eng
License: None

Project Name: closed-loop-acceleration-benchmarks
Github URL: aced-differentiate/closed-loop-acceleration-benchmarks
Category: materials-discovery
Labels: materials-discovery, active-learning, single-paper
License: MIT
Package Managers:

Project Name: EquivariantOperators.jl
Github URL: aced-differentiate/EquivariantOperators.jl
Category: math
Labels: math, symmetrized, lang-julia
License: MIT

Project Name: Closed-loop acceleration benchmarks
Github URL: aced-differentiate/closed-loop-acceleration-benchmarks
Category: materials-discovery
Labels: materials-discovery, active-learning, single-paper
License: MIT
Package Managers:

Project Name: Citrination Datasets
URL: https://citrination.com/datasets
Category: datasets
Labels: datasets
License: MIT
Package Managers: None.

Add project: mat2vec

Project details:

Project Name: mat2vec
Github URL: https://github.com/materialsintelligence/mat2vec
Category: representations
License: MIT
Package Managers: None

Additional context:

https://www.nature.com/articles/s41586-019-1335-8#data-availability

Add project: MEGAN

Project details:

Project Name: MEGAN: Multi Explanation Graph Attention Student
Github URL: aimat-lab/graph_attention_student
Category: xai
Labels: xai, rep-learn
License: MIT
Package Managers: None

Additional context:

Original publication: https://arxiv.org/abs/2305.15961v1

(technically, the original publication is http://arxiv.org/abs/2211.13236, but the two papers link to repos that do not exist anymore or have been updated since. therefore, assume the newer publication is the closest one to current state of code.)

Update project: Flare

Update details:

Project Name: Flare (https://github.com/mir-group/flare)

Additional context:

The project is listed under General Tools. I think a better fit would be the Active Learning section since a core functionality is building force fields using AL.

Add project: escnn_jax

Project details:

Project Name: escnn_jax
Github URL: https://github.com/emilemathieu/escnn_jax
Category: representation-learning
License: https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE
Package Managers: pypi:escnn_jax

Additional context:

via https://twitter.com/MathieuEmile/status/1674809578611982338

Add project: paperswithcode

Project details:

This suggestion is not a single project, but several datasets and benchmark models aggregated via paperswithcode.

For some of the already existing list entries (e.g. QM9, OC20 dataset), maybe replace website with paperswithcode entry.
The paperswithcode tags could be entered as resource in respective category, if any. Collect here first, then assign.
Also add the linked models / packages.

Initial datasets finds.

Initial tags finds.

Add project: Materials Intelligence's projects

Project details:

AML projects by https://github.com/materialsintelligence

Add project: Atom2Vec

Project details:

Project Name: Atom2Vec
Github URL: https://github.com/idocx/Atom2Vec
Category: representations
License: None
Package Managers: pypi:atom2vec

Additional context:

https://www.pnas.org/doi/10.1073/pnas.1801181115

Note: This seems to be a reimplementation.

Add project: Matter Modeling Stack Exchange - ML

Project details:

Project Name: Matter Modeling Stack Exchange - Machine Learning
Homepage: https://mattermodeling.stackexchange.com/questions/tagged/machine-learning
Category: community

Add project: DIVE Lab's projects

Additional context:

Projects by DIVE Lab.

Add project: mlp (Proper orthogonal descriptors, POD)

Project details:

Project Name: mlp
Description: "Proper orthogonal descriptors for efficient and accurate interatomic potentials. Paper."
Github URL: https://github.com/cesmix-mit/mlp
Category: mlp
Labels: lang-julia
License: None
Package Managers: None

Additional context:

POD descriptor benchmark, as referenced in the corresponding paper https://doi.org/10.1016/j.jcp.2023.112030.

Original publication not linked in README as of 2023-12-03. Repo description not descriptive. Replace with custom description: Paper title plus link to paper.

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judftteam / best-of-atomistic-machine-learning Goto Github PK

best-of-atomistic-machine-learning's Introduction

Best of Atomistic Machine Learning ⚛️🧬💎

Contents

Explanation

Active learning

Biomolecules

Community resources

Datasets

Data Structures

Density functional theory (ML-DFT)

Educational Resources

Explainable Artificial intelligence (XAI)

Electronic structure methods (ML-ESM)

General Tools

Generative Models

Interatomic Potentials (ML-IAP)

Language Models

Materials Discovery

Mathematical tools

Molecular Dynamics

Reinforcement Learning

Representation Engineering

Representation Learning

Unsupervised Learning

Visualization

Wavefunction methods (ML-WFT)

Others

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