41 citations · 107 across the 6 of their papers we have counts for
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Insight into the effect of force error on the thermal conductivity from machine-learned potentials
Wenjiang Zhou, Nianjie Liang, Xiguang Wu +3
Machine-learned potentials (MLPs) have been extensively used to obtain the lattice thermal conductivity via atomistic simulations. However, the impact of force errors in various ML…
Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
Penghua Ying, Wenjiang Zhou, Lucas Svensson +10
Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance o…
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
Xiguang Wu, Wenjiang Zhou, Haikuang Dong +5
Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs genera…