atomistic-machine-learning/schnetpack

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

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

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

Graph deep learning library for materials

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.

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

Torch-native, batchable, atomistic simulations.

Message Passing Neural Networks for Molecule Property Prediction

FAIR Chemistry's library of machine learning methods for chemistry