Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential

被引：0

作者：

Wu J. ^{[1
,2
]}

Huang A. ^{[1
]}

Xie H. ^{[1
]}

Wei D. ^{[1
]}

Li A. ^{[1
]}

Peng B. ^{[1
]}

Wang H. ^{[3
]}

Qin Z. ^{[4
]}

Liu T.-H. ^{[2
]}

Qin G. ^{[1
]}

机构：

[1] State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha

[2] School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan

[3] Hunan Key Laboratory for Micro-Nano Energy Materials & Device, School of Physics and Optoelectronics, Xiangtan University, Hunan, Xiangtan

[4] School of Physics and Microelectronics, Zhengzhou University, Zhengzhou

来源：

Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society | 2023年 / 51卷 / 02期

关键词：

atomic interaction potential; machine learning; mechanical; multiscale; thermal properties;

D O I：

10.14062/j.issn.0454-5648.20220826

中图分类号：

学科分类号：

摘要：

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. © 2023 Chinese Ceramic Society. All rights reserved.

引用

页码：531 / 543

页数：12

共 112 条

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