Accuracy of Machine Learning Potential for Predictions of Multiple-Target Physical Properties
Yulou Ouyang1 , Zhongwei Zhang1 , Cuiqian Yu1 , Jia He1 , Gang Yan1,2 , and Jie Chen1*
1 Center for Phononics and Thermal Energy Science, China–EU Joint Lab for Nanophononics, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China2 Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
Abstract ：The accurate and rapid prediction of materials' physical properties, such as thermal transport and mechanical properties, are of particular importance for potential applications of featuring novel materials. We demonstrate, using graphene as an example, how machine learning potential, combined with the Boltzmann transport equation and molecular dynamics simulations, can simultaneously provide an accurate prediction of multiple-target physical properties, with an accuracy comparable to that of density functional theory calculation and/or experimental measurements. Benchmarked quantities include the Grüneisen parameter, the thermal expansion coefficient, Young's modulus, Poisson's ratio, and thermal conductivity. Moreover, the transferability of commonly used empirical potential in predicting multiple-target physical properties is also examined. Our study suggests that atomic simulation, in conjunction with machine learning potential, represents a promising method of exploring the various physical properties of novel materials.
收稿日期: 2020-09-26
出版日期: 2020-12-08
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2020, Vol. 37 
