Yanzhou Wang

Lanzhou University of Technology — China

Materials Science · Physical Sciences

17h-index975citations60works11.72yr avg
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Research Topics

Machine Learning in Materials Science(23), Thermal properties of materials(13), Perovskite Materials and Applications(11), Quantum Dots Synthesis And Properties(7), 2D Materials and Applications(6)

Publications60 total

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
2025· 2 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 the Photovoltaic Performance of Carbon-Based Hole-Transport-Layer-Free CsPbI<sub>2</sub>Br Solar Cells via Trifluoromethyl Thianthrenium Triflate Additive
ACS Applied Energy Materials·2025
Insight into the diversity and application characters of endophytes in ramie (Boehmeria nivea L. Gaud)
Industrial Crops and Products·2025Open Access
Structural and transport properties of LiTFSI/G3 electrolyte with machine-learned molecular dynamics
arXiv (Cornell University)·2025Open Access
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
Physical review. B./Physical review. B·2025· 10 cited
Publisher's Note: “Advances in modeling complex materials: The rise of neuroevolution potentials” [Chem. Phys. Rev. <b>6</b>, 011310 (2025)]
Chemical Physics Reviews·2025· 1 citedOpen Access
Advances in modeling complex materials: The rise of neuroevolution potentials
Chemical Physics Reviews·2025· 25 citedOpen Access
Advances in modeling complex materials: The rise of neuroevolution potentials
arXiv (Cornell University)·2025· 2 citedOpen Access
Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics
Scopus (Elsevier)·2024· 2 citedOpen Access
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
The Journal of Chemical Physics·2024· 33 cited
Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials
Journal of Applied Physics·2024· 75 citedOpen Access
Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
Journal of Physics Condensed Matter·2024· 22 citedOpen Access
Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations
International Journal of Heat and Mass Transfer·2024· 12 cited
Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics
arXiv (Cornell University)·2024Open Access
General-purpose machine-learned potential for 16 elemental metals and their alloys
2023· 2 citedOpen Access
General-purpose machine-learned potential for 16 elemental metals and their alloys
arXiv (Cornell University)·2023· 6 citedOpen Access
Notable Performance Enhancement of CsPbI<sub>2</sub>Br Solar Cells by a Dual-Function Strategy with CsPbBr<sub>3</sub> Nanocrystals
ACS Applied Materials & Interfaces·2023· 12 cited
Interfacial Treatment by 1,4-Phenyldiammonium Diiodide to Significantly Improve the Performance of Carbon-Based CsPbI<sub>2</sub>Br Solar Cells
ACS Applied Electronic Materials·2023· 11 cited
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Frequent Co-authors

Zheyong Fan(22), Ping Qian(17), Junshuai Li(14), Qiming Liu(13), Deyan He(13), Tapio Ala-Nissilä(13), Yali Li(11), Penghua Ying(11), Haikuan Dong(9), Shunda Chen(9), Yujun Fu(9), Weining Liu(7), Zhe Gao(7), Xincheng Yao(6), A. Miguel(6), Ke Xu(6), Qiulu Chen(6), Yanjing Su(6), Keke Song(5), Zezhu Zeng(4)