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

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.

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

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

Multidimensional data analysis

FiPy is a Finite Volume PDE solver written in Python

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

Data mining for materials science

Density-functional toolkit

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

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

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

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

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

Graph deep learning library for materials

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

Scientific analysis of nanoscale materials imaging data

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

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