Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials

被引:8
作者
Fan, Zheyong [1 ]
Xiao, Yang [1 ]
Wang, Yanzhou [2 ]
Ying, Penghua [3 ]
Chen, Shunda [4 ]
Dong, Haikuan [1 ]
机构
[1] Bohai Univ, Coll Phys Sci & Technol, Jinzhou 121013, Peoples R China
[2] Aalto Univ, QTF Ctr Excellence, Dept Appl Phys, MSP Grp, FI-00076 Espoo, Finland
[3] Tel Aviv Univ, Sch Chem, Dept Phys Chem, IL-6997801 Tel Aviv, Israel
[4] George Washington Univ, Dept Civil & Environm Engn, Washington, DC 20052 USA
基金
中国国家自然科学基金;
关键词
machine-learned neuroevolution potential; molecular dynamics; thermal transport; linear-scaling quantum transport; electron-phonon scattering; electronic transport; thermoelectric transport; THERMOELECTRIC PROPERTIES;
D O I
10.1088/1361-648X/ad31c2
中图分类号
O469 [凝聚态物理学];
学科分类号
070205 ;
摘要
We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset.
引用
收藏
页数:8
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