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Haikuan Dong

Showing results (1-10 of 12) with videos related to

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Physical Chemistry Chemical Physics : PCCP|September 20, 2018
Heat transport in pristine and polycrystalline single-layer hexagonal boron nitrideHaikuan Dong, Petri Hirvonen, Zheyong Fan, et al.
Journal of Physics. Condensed Matter : an Institute of Physics Journal|April 29, 2024
Thermal conductivity of irregularly shaped nanoparticles from equilibrium molecular dynamicsHongfei Li, Yuanxu Zhu, MengFan Chu, et al.
Physical Review Letters|July 8, 2022
Barbalinardo et al. ReplyGiuseppe Barbalinardo, Zekun Chen, Haikuan Dong, et al.
Physical Review Letters|July 23, 2021
Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic ModelingGiuseppe Barbalinardo, Zekun Chen, Haikuan Dong, et al.
Physical Chemistry Chemical Physics : PCCP|November 7, 2023
Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentialsYongbo Shi, Yuanyuan Chen, Haikuan Dong, et al.
Journal of Physics. Condensed Matter : an Institute of Physics Journal|March 8, 2024
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materialsZheyong Fan, Yang Xiao, Yanzhou Wang, et al.
The Journal of Chemical Physics|July 1, 2024
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamicsXiguang Wu, Wenjiang Zhou, Haikuan Dong, et al.
The Journal of Chemical Physics|April 16, 2026
Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulationsBohan Zhang, Biyuan Liu, Penghua Ying, 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.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Physical Chemistry Chemical Physics : PCCP|September 20, 2018
Heat transport in pristine and polycrystalline single-layer hexagonal boron nitrideHaikuan Dong, Petri Hirvonen, Zheyong Fan, et al.
Journal of Physics. Condensed Matter : an Institute of Physics Journal|April 29, 2024
Thermal conductivity of irregularly shaped nanoparticles from equilibrium molecular dynamicsHongfei Li, Yuanxu Zhu, MengFan Chu, et al.
Physical Review Letters|July 8, 2022
Barbalinardo et al. ReplyGiuseppe Barbalinardo, Zekun Chen, Haikuan Dong, et al.
Physical Review Letters|July 23, 2021
Ultrahigh Convergent Thermal Conductivity of Carbon Nanotubes from Comprehensive Atomistic ModelingGiuseppe Barbalinardo, Zekun Chen, Haikuan Dong, et al.
Physical Chemistry Chemical Physics : PCCP|November 7, 2023
Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentialsYongbo Shi, Yuanyuan Chen, Haikuan Dong, et al.
Journal of Physics. Condensed Matter : an Institute of Physics Journal|March 8, 2024
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materialsZheyong Fan, Yang Xiao, Yanzhou Wang, et al.
The Journal of Chemical Physics|July 1, 2024
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamicsXiguang Wu, Wenjiang Zhou, Haikuan Dong, et al.
The Journal of Chemical Physics|April 16, 2026
Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulationsBohan Zhang, Biyuan Liu, Penghua Ying, 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.
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