activity
20222026
most citedGPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

463 citations · 699 across the 8 of their papers we have counts for

collaborators
Showing physics.comp-phShow all

2 papers · 1 filter

physics.comp-ph2023★ 2 cited

Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten

Jiahui Liu, Jesper Byggmastar, Zheyong Fan +2

Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning b…

physics.comp-ph2022★ 463 cited

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

Zheyong Fan, Yanzhou Wang, Penghua Ying +17

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…