期刊
CHINESE PHYSICS LETTERS
卷 37, 期 12, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/0256-307X/37/12/126301
关键词
63; 20; dk; 63; 20; kg
资金
- National Natural Science Foundation of China [12075168, 11890703]
- Science and Technology Commission of Shanghai Municipality [19ZR1478600, 18ZR1442000, 18JC1410900]
- Fundamental Research Funds for the Central Universities [22120200069]
- Open Fund of Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion [2018TP1037_201901]
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 Gruneisen 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.
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