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Directed message passing neural networks for property prediction of molecules and reactions with uncertainty and interpretation.

Python Library for Automating Molecular Simulation: input preparation, job execution, file management, output processing and building data workflows.

PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)

A library for processing, analyzing and modeling spectroscopic data.

A package to 'build' collections of materials properties from the output of computational materials calculations.

A molecule manipulation library.

atomate2 is a library of computational materials science workflows.

This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.

Universal pretrained neural network potential with charge and magnetic moment awareness, trained on 1.5M+ Materials Project inorganic structures for charge-informed molecular dynamics and phase diagram prediction (Berkeley, Nature Machine Intelligence 2023 Cover)

NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)

Descriptor computation(chemistry) and (optional) storage for machine learning.