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Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential
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Jing WU1, 2, An HUANG1, Hanpeng XIE1, Donghai WEI1, Aonan LI1, Bo PENG1, Huimin WANG3, Zhenzhen QIN4, Te-huan LIU2, Guangzhao QIN1
Journal of the Chinese Ceramic Society | 2023, 51(2) : 531 - 543
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Journal of the Chinese Ceramic Society | 2023, 51(2): 531-543
• Review •
Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential
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Jing WU1, 2, An HUANG1, Hanpeng XIE1, Donghai WEI1, Aonan LI1, Bo PENG1, Huimin WANG3, Zhenzhen QIN4, Te-huan LIU2, Guangzhao QIN1
Affiliations
  • 1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
  • 2.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 3.Hunan Key Laboratory for Micro-Nano Energy Materials & Device and School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, Hunan, China
  • 4.School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China

    • WU Jing (1998-), male, Doctoral candidate. E-mail:

About Author:

WU Jing (1998-), male, Doctoral candidate. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220826
Outline
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With the development of artificial intelligence technology, machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential. Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research. This review represented the characteristics of existing machine learning potential and the applications in phase change, intrinsic properties and interface researches. In addition, the challenge and development trends of machine learning atomic interaction potential were also prospected.

machine learning  /  atomic interaction potential  /  multiscale  /  mechanical and thermal properties
吴静, 黄安, 谢涵鹏, 魏东海, 李奥南, 彭博, 王慧敏, 秦真真, 刘德欢, 秦光照. 基于机器学习势函数的材料力热性质多尺度模拟研究进展. 硅酸盐学报, 2023 , 51 (2) : 531 -543 . DOI: 10.14062/j.issn.0454-5648.20220826
Jing WU, An HUANG, Hanpeng XIE, Donghai WEI, Aonan LI, Bo PENG, Huimin WANG, Zhenzhen QIN, Te-huan LIU, Guangzhao QIN. Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 531 -543 . DOI: 10.14062/j.issn.0454-5648.20220826
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Year 2023 volume 51 Issue 2
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Article Info
doi: 10.14062/j.issn.0454-5648.20220826
  • Receive Date：2022-10-01
  • Online Date：2026-10-20
  • Published：2023-02-20
Article Data
Affiliations
History
  • Received：2022-10-01
  • Revised：2022-11-04
Funding
Affiliations
    1.State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
    2.School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    3.Hunan Key Laboratory for Micro-Nano Energy Materials & Device and School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, Hunan, China
    4.School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China

Corresponding:

LIU Te-huan (1984-), male, Ph.D., Professor. E-mail:
QIN Guangzhao (1991-), male, Ph.D., Professor.
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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