mir-group/nequip

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

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.

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

Torch-native, batchable, atomistic simulations.

Graph deep learning library for materials

ORB forcefield models from Orbital Materials

An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations

New PYRAIMD updated from 2021 version developed from Nanoacademic Technology Inc.