materials-science

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

Data mining for materials science

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

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.

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

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

Graph deep learning library for materials

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

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.

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