MDIL-SNU/SevenNet

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

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

An open-source Python package for creating fast and accurate interatomic potentials.

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

Torch-native, batchable, atomistic simulations.

SchNetPack - Deep Neural Networks for Atomistic Systems

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.

Universal machine-learning models for advanced atomistic simulations

Code for automated fitting of machine learned interatomic potentials.