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
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
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…