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

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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…

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…