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

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Showing 2023Show all

2 papers · 1 filter

cond-mat.mtrl-sci2023★ 203 cited

General-purpose machine-learned potential for 16 elemental metals and their alloys

Keke Song, Rui Zhao, Jiahui Liu +25

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…

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…