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A high-performance toolkit for atomistic simulations in JAX.
An agentic skills framework & software development methodology that works.
CatBench - Benchmark Framework of Machine Learning Interatomic Potentials in Adsorption Energy Predictions
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Benchmarking and validation of Machine Learning Interatomic Potentials (MLIP)
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
NequIP is a code for building E(3)-equivariant interatomic potentials
Rich is a Python library for rich text and beautiful formatting in the terminal.
🏆 A ranked list of awesome python developer tools and libraries. Updated weekly.
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
An interactive structure/property explorer for materials and molecules
🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/forum?id=SAT0KPA5UO
Container runtimes on macOS (and Linux) with minimal setup
GitHub Actions for GitHub Pages 🚀 Deploy static files and publish your site easily. Static-Site-Generators-friendly.
Library for efficient training and application of Machine Learning Interatomic Potentials (MLIP)
Jupyter widget to interactively view molecular structures and trajectories
OpenEquivariance: a fast, open-source GPU JIT kernel generator for the Clebsch-Gordon Tensor Product.
Create powerful Hydra applications without the yaml files and boilerplate code.
FAIR Chemistry's library of machine learning methods for chemistry
A Python API for the RCSB Protein Data Bank (PDB)
An evaluation framework for machine learning models simulating high-throughput materials discovery.
MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
The analyses applied on high-dimensional performance data of machine learning interatomic potentials (MLIPs), described in 'Learning from models: high-dimensional analyses on the performance of MLIPs'
Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIPs). It offers a growing set of evaluation methods alongside …