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Zheyong Fan

Showing results (31-40 of 37) with videos related to

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Nanoscale|May 26, 2021
Anomalous thermal conductivity enhancement in low dimensional resonant nanostructures due to imperfectionsHongying Wang, Yajuan Cheng, Zheyong Fan, et al.
Small (Weinheim an Der Bergstrasse, Germany)|March 7, 2025
Stress-Driven Grain Boundary Structural Transition in Diamond by Machine Learning PotentialChenchen Lu, Zhen Li, Xinxin Sang, et al.
The Journal of Chemical Physics|February 12, 2025
Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materialsPenghua Ying, Wenjiang Zhou, Lucas Svensson, et al.
Journal of Chemical Theory and Computation|April 20, 2026
qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic SimulationsZheyong Fan, Benrui Tang, Esmée Berger, et al.
Nature Computational Science|July 8, 2026
NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elementsTing Liang, Ke Xu, Eric Lindgren, et al.
The Journal of Chemical Physics|September 22, 2022
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulationsZheyong Fan, Yanzhou Wang, Penghua Ying, et al.
Nature Communications|November 25, 2024
General-purpose machine-learned potential for 16 elemental metals and their alloysKeke Song, Rui Zhao, Jiahui Liu, et al.
Pageof 4

Showing results (31-40 of 37) with videos related to

Sort By:
Pageof 4
You have reached the last page of results.This site can display upto 37 results.
Nanoscale|May 26, 2021
Anomalous thermal conductivity enhancement in low dimensional resonant nanostructures due to imperfectionsHongying Wang, Yajuan Cheng, Zheyong Fan, et al.
Small (Weinheim an Der Bergstrasse, Germany)|March 7, 2025
Stress-Driven Grain Boundary Structural Transition in Diamond by Machine Learning PotentialChenchen Lu, Zhen Li, Xinxin Sang, et al.
The Journal of Chemical Physics|February 12, 2025
Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materialsPenghua Ying, Wenjiang Zhou, Lucas Svensson, et al.
Journal of Chemical Theory and Computation|April 20, 2026
qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic SimulationsZheyong Fan, Benrui Tang, Esmée Berger, et al.
Nature Computational Science|July 8, 2026
NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elementsTing Liang, Ke Xu, Eric Lindgren, et al.
The Journal of Chemical Physics|September 22, 2022
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulationsZheyong Fan, Yanzhou Wang, Penghua Ying, et al.
Nature Communications|November 25, 2024
General-purpose machine-learned potential for 16 elemental metals and their alloysKeke Song, Rui Zhao, Jiahui Liu, et al.
Pageof 4