mlff

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

Python package designed to run atomistic Monte Carlo simulations.

Interface materials design toolkit

Knowledge library and MLOps of foundation ML for chemistry and drug discovery - Let's develop, train, optimize, and deploy models at scale

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

Green-Kubo lattice thermal conductivity from a VASP MLFF ML_HEAT heat-flux trajectory

End-to-end digital twin of a multi-lane free-flow tolling gantry: roadside sensing, cabinet, fusion, rating, acceptance. Synthetic data.

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.