Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

A deep learning package for many-body potential energy representation and molecular dynamics

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.

Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.

NequIP is a code for building E(3)-equivariant interatomic potentials

FiPy is a Finite Volume PDE solver written in Python

Multidimensional data analysis

Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials

Graph deep learning library for materials

CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.

[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.

A toolkit for visualizations in materials informatics.

[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs

atomate2 is a library of computational materials science workflows

Heavyweight plotting tools for ab initio calculations

Deep and Machine Learning for Microscopy

RadonPy is a Python library to automate physical property calculations for polymer informatics.

Matbench: Benchmarks for materials science property prediction