materialyzeai/matgl

SchNetPack - Deep Neural Networks for Atomistic Systems

FAIR Chemistry's library of machine learning methods for chemistry

MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.

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

Graphormer is a general-purpose deep learning backbone for molecular modeling.

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

Data mining for materials science

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

TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained by the Roitberg group.