Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910398721032192 |
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| author | Fan, Zheyong Xiao, Yang Wang, Yanzhou Ying, Penghua Chen, Shunda Dong, Haikuan |
| author_facet | Fan, Zheyong Xiao, Yang Wang, Yanzhou Ying, Penghua Chen, Shunda Dong, Haikuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15314 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials Fan, Zheyong Xiao, Yang Wang, Yanzhou Ying, Penghua Chen, Shunda Dong, Haikuan Materials Science 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. |
| title | Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2310.15314 |