Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
| Autor: | Fan, Zheyong, Xiao, Yang, Wang, Yanzhou, Ying, Penghua, Chen, Shunda, Dong, Haikuan |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | J. Phys.: Condens. Matter, 2024, 36, 245901 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1088/1361-648X/ad31c2 |
| Popis: | 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 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 thermoelectric transport properties of a graphene antidot lattice. Comment: 8 pages, 4 figures |
| Databáze: | arXiv |
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