| Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport Z Fan, Z Zeng, C Zhang, Y Wang, K Song, H Dong, Y Chen, T Ala-Nissila Physical Review B 104 (10), 104309, 2021 | 173 | 2021 |
| GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations Z Fan, Y Wang, P Ying, K Song, J Wang, Y Wang, Z Zeng, K Xu, ... The Journal of Chemical Physics 157 (11), 2022 | 116 | 2022 |
| Elastic behavior and intrinsic carrier mobility for monolayer SnS and SnSe: First-principles calculations LB Shi, M Yang, S Cao, Q You, YY Niu, YZ Wang Applied Surface Science 492, 435-448, 2019 | 45 | 2019 |
| Structure and Pore Size Distribution in Nanoporous Carbon Y Wang, Z Fan, P Qian, T Ala-Nissila, MA Caro https://pubs.acs.org/doi/full/10.1021/acs.chemmater.1c03279 34 (2), 617-628, 2022 | 43 | 2022 |
| Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations Y Wang, Z Fan, P Qian, MA Caro, T Ala-Nissila Physical Review B 107 (5), 054303, 2023 | 38 | 2023 |
| A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties Z Fan, Y Wang, X Gu, P Qian, Y Su, T Ala-Nissila Journal of Physics: Condensed Matter 32 (13), 135901, 2019 | 22 | 2019 |
| Size and stoichiometry effect of FePt bimetal nanoparticle catalyst for CO oxidation: A DFT study L Li, YZ Wang, XX Wang, KK Song, XD Jian, P Qian, Y Bai, YJ Su The Journal of Physical Chemistry C 124 (16), 8706-8715, 2020 | 18 | 2020 |
| An investigation on carrier transport behavior of tetragonal halide perovskite: First-principles calculation Y Su, H Wang, LB Shi, YZ Wang, Q Liu, P Qian Materials Science in Semiconductor Processing 150, 106836, 2022 | 9 | 2022 |
| Theoretical prediction of intrinsic carrier mobility of monolayer C7N6: First-principles study Y Zhang, S Cao, Y Wang, X Jian, L Shi, P Qian Physics Letters A 401, 127340, 2021 | 7 | 2021 |
| Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials H Dong, Y Shi, P Ying, K Xu, T Liang, Y Wang, Z Zeng, X Wu, W Zhou, ... Journal of Applied Physics 135 (16), 2024 | 6 | 2024 |
| General-purpose machine-learned potential for 16 elemental metals and their alloys K Song, R Zhao, J Liu, Y Wang, E Lindgren, Y Wang, S Chen, K Xu, ... arXiv preprint arXiv:2311.04732, 2023 | 6 | 2023 |
| Study of oxygen evolution reaction on amorphous Au 13@ Ni 120 P 50 nanocluster Y Wang, P Gao, X Wang, J Huo, L Li, Y Zhang, AA Volinsky, P Qian, Y Su Physical Chemistry Chemical Physics 20 (21), 14545-14556, 2018 | 6 | 2018 |
| Investigation of carrier transport behavior for cubic CH3NH3SnX3 and CH3NH3PbX3 (X= Br and I) using Boltzmann transport equation Y Su, N Li, LB Shi, YZ Wang, P Qian Computational Materials Science 213, 111609, 2022 | 5 | 2022 |
| Catalytic activity, thermal stability and structural evolution of PdCu single-atom alloy catalysts: the effects of size and morphology Q Liu, X Wang, L Li, K Song, Y Wang, P Qian RSC advances 12 (1), 62-71, 2022 | 5 | 2022 |
| Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics X Wu, W Zhou, H Dong, P Ying, Y Wang, B Song, Z Fan, S Xiong The Journal of Chemical Physics 161 (1), 2024 | 3 | 2024 |
| Thermal transports of 2D phosphorous carbides by machine learning molecular dynamics simulations C Cao, S Cao, YX Zhu, H Dong, Y Wang, P Qian International Journal of Heat and Mass Transfer 224, 125359, 2024 | 2 | 2024 |
| General-purpose machine-learned potential for 16 elemental metals and their alloys Z Fan, K Song, R Zhao, J Liu, Y Wang, E Lindgren, Y Wang, S Chen, K Xu, ... | 1 | 2023 |
| Mechanical and thermodynamic properties study of Al-based binary and ternary solid solutions using the pseudoatomic potential method JR Huo, K Wang, YZ Wang, P Qian, C Ji, Y Su Intermetallics 126, 106931, 2020 | 1 | 2020 |
| Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics | | |
| GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations Zheyong Fan, Yanzhou Wang, 2, 3 Penghua Ying, 4 Keke … | | |
Zheyong FanBohai University在 aalto.fi 的电子邮件经过验证
