most citedImproving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials

1 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cond-mat.mtrl-sci2026

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Zihan Yan, Denan Li, Xin Wu +20

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics proce…

physics.comp-ph2026★ 1 cited

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations

Zheyong Fan, Benrui Tang, Esmée Berger +13

Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time sim…

cond-mat.mtrl-sci2025★ 1 cited

Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials

Zihan Yan, Zheyong Fan, Yizhou Zhu

Machine learning force fields (MLFFs) are powerful tools for materials modeling, but their performance is often limited by training dataset quality, particularly the lack of rare e…