MatterSim: A deep learning atomistic model across elements, temperatures and pressures.
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
Model zoo and experimental features of machine learning interatomic potentials.
Information of foundation ML for chemistry and drug discovery - Let's develop, train, optimize, and deploy models at scale
Optimize and deploy ALCHEMI models on NVIDIA NIM model serving platform
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.
Green-Kubo lattice thermal conductivity from a VASP MLFF ML_HEAT heat-flux trajectory
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.
End-to-end digital twin of a multi-lane free-flow tolling gantry: roadside sensing, cabinet, fusion, rating, acceptance. Synthetic data.
Development of machine learning force field for Dialanine
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