MatterSim: A deep learning atomistic model across elements, temperatures and pressures.

GAIA automates the generation of reactive MLIP datasets for atomistic simulations.

Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093

Interface materials design toolkit

Model zoo and experimental features of machine learning interatomic potentials.

Optimize and deploy ALCHEMI models on NVIDIA NIM model serving platform

A update QM9 benchmark, update form the recent articles

Open machine-learning force field (MLFF) training datasets for pristine, defect-engineered, doped, and interfacial HOPG systems generated from first-principles Density Functional Theory (DFT) calculations.

Development of machine learning force field for Dialanine

An E(3)-equivariant atomistic graph neural network that couples local chemical interactions, differentiable electrostatic and polarization physics, and a time-reversal-aware spin Hamiltonian in one trainable model.