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

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Data mining for materials science

Density-functional toolkit

Curated list of known efforts in materials informatics, i.e. in modern materials science

DScribe is a python package for creating machine learning descriptors for atomistic systems.

Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

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

MatterSim: A deep learning atomistic model across elements, temperatures and pressures.

Cross platform, open source application for the processing, visualization, and analysis of 3D tomography data

Graph deep learning library for materials

A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

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.