@article{WU2023, 
author = {Jing WU and An HUANG and Hanpeng XIE and Donghai WEI and Aonan LI and Bo PENG and Huimin WANG and Zhenzhen QIN and Te-huan LIU and Guangzhao QIN},
title = {Multi-Scale Simulation of Mechanical and Thermal Transport Properties of Materials Based on Machine Learning Potential},
year = {2023},
journal = {Journal of the Chinese Ceramic Society},
volume = {51},
number = {2},
pages = {531-543},
keywords = {machine learning, multiscale, mechanical and thermal properties, atomic interaction potential},
url = {https://www.sciopen.com/article/10.14062/j.issn.0454-5648.20220826},
doi = {10.14062/j.issn.0454-5648.20220826},
abstract = {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.}
}