Eight datasets (including two training datasets, two validation datasets, and four testing datasets) for the publication "Assessing the accuracy of MLIPs in predicting the elemental ordering: a cas…

Training, validation, and testing datasets for the publication "The discrepancies, the extrapolability, and the interpolability of machine learning interatomic potentials on simulating atomic dynam…

Test interatomic potential by Benchmarking with several physical properties using LAMMPS

I-ReaxFF: stand for Intelligent-Reactive Force Field

A collection of tools and databases for atomistic machine learning

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

Ab initio simulator for thermal transport and lattice anharmonicity

Phonon code

A grain boundary generation code

Interface aenet with the Tinker molecular dynamics software

Python interface for aenet

Graph deep learning library for materials

Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.

ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training

Public development project of the LAMMPS MD software package