Computational Chemistry

TorchMD-Net implements trainable neural network potentials as PyTorch modules, with TensorNet architectures, custom molecular datasets, pretrained checkpoint loading and molecular dynamics integrations.

Molecular Property Prediction · Computational Chemistry

DeePMD-kit is a toolkit for training and fine-tuning Deep Potential interatomic models from quantum-mechanical reference data, then exporting them for inference and molecular dynamics.

Computational Chemistry · Materials Discovery

Allegro implements an E(3)-equivariant interatomic potential as a NequIP extension, with documented GPU acceleration options and a separate plugin for LAMMPS simulations.

Computational Chemistry · Materials Discovery

NequIP is an open-source framework for building E(3)-equivariant interatomic potentials, with training, pre-trained foundation potentials, and integration with ASE and LAMMPS.

Computational Chemistry · Materials Discovery

MACE provides higher-order equivariant machine-learning interatomic potentials, with workflows for training on atomistic data, evaluating configurations, and using or fine-tuning pretrained models.

Computational Chemistry · Materials Discovery