Zheyong Fan

Bohai University — China

Materials Science · Physical Sciences

39h-index5.2kcitations226works8.12yr avg
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Research Topics

Thermal properties of materials(87), Machine Learning in Materials Science(78), Graphene research and applications(54), Advanced Thermoelectric Materials and Devices(38), Carbon Nanotubes in Composites(13)

Publications226 total

Anisotropic and isotropic elasticity and thermal transport in monolayer C24 networks from machine-learning molecular dynamics
International Journal of Heat and Mass Transfer·2026Open Access
Modular hybrid machine learning and physics-based potentials for scalable modeling of Van der Waals heterostructures
Journal of the Mechanics and Physics of Solids·2026Open Access
Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures
npj Computational Materials·2025· 2 citedOpen Access
Heat transport in superionic materials via machine-learned molecular dynamics
arXiv (Cornell University)·2025Open Access
Thermal cycling reliability of hybrid pixel sensor modules for the ATLAS High Granularity Timing Detector
Journal of Instrumentation·2025Open Access
Achieving Empirical Potential Efficiency with DFT Accuracy: A Neuroevolution Potential for the $α$-Fe--C--H System
arXiv (Cornell University)·2025Open Access
Lattice distortion leads to glassy thermal transport in crystalline Cs <sub>3</sub> Bi <sub>2</sub> I <sub>6</sub> Cl <sub>3</sub>
Proceedings of the National Academy of Sciences·2025· 2 citedOpen Access
GPUTB: Efficient machine learning tight-binding method for large-scale electronic properties calculations
Computational Materials Today·2025Open Access
NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials
Computer Physics Communications·2025· 9 cited
GPUTB: Efficient Machine Learning Tight-Binding Method for Large-Scale Electronic Properties Calculations
arXiv (Cornell University)·2025Open Access
NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties
npj Computational Materials·2025· 6 citedOpen Access
Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations
Physical Review Materials·2025· 3 cited
PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations
Journal of Applied Physics·2025· 12 citedOpen Access
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
2025· 2 citedOpen Access
GPUMD 4.0: A high‐performance molecular dynamics package for versatile materials simulations with machine‐learned potentials
Materials Genome Engineering Advances·2025· 38 citedOpen Access
Revealing the impact of chemical short-range order on radiation damage in MoNbTaVW high-entropy alloys using a machine-learning potential
arXiv (Cornell University)·2025Open Access
Structural and Transport Properties of LiTFSI/G<sub>3</sub> Electrolyte with Machine-Learned Molecular Dynamics
The Journal of Physical Chemistry C·2025· 3 cited
Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint
npj Computational Materials·2025· 1 citedOpen Access
Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy
Journal of Nuclear Materials·2025· 7 cited
Thermal Transport of Amorphous Hafnia Across the Glass Transition
ACS Materials Letters·2025· 1 citedOpen Access
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Frequent Co-authors

Tapio Ala-Nissilä(17), Ari Harju(15), Haikuan Dong(11), Penghua Ying(10), Ke Xu(9), Ting Liang(7), Zezhu Zeng(6), Yanjing Su(6), Yue Chen(5), Paul Erhart(5), Jin‐Cheng Zheng(5), Davide Donadio(5), Ning Wei(5), Ping Qian(5), Shiyun Xiong(5), Alexander J. Gabourie(4), Jiahui Liu(4), Luiz Felipe C. Pereira(4), Hui‐Qiong Wang(4), Petri Hirvonen(4)