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
Interface materials design toolkit
Information 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
Development of machine learning force field for Dialanine
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.
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.
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