Repository for links to software packages and databases used in deep-learning applications for materials science

JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ https://www.youtube.com/@dr_k_choudhary

Deep learning framework for atomistic image data

Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary

Crystal graph convolutional neural networks for predicting material properties.

An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]

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 c…