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

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date

2024-06-19

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Department of Applied Physics

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Language

en

Pages

8

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Journal of Physics Condensed Matter, Volume 36, issue 24, pp. 1-8

Abstract

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.

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Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd

Keywords

electron-phonon scattering, electronic transport, linear-scaling quantum transport, machine-learned neuroevolution potential, molecular dynamics, thermal transport, thermoelectric transport

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DOI

10.1088/1361-648X/ad31c2

Citation

Fan, Z, Xiao, Y, Wang, Y, Ying, P, Chen, S & Dong, H 2024, 'Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials', Journal of Physics Condensed Matter, vol. 36, no. 24, 245901, pp. 1-8. https://doi.org/10.1088/1361-648X/ad31c2

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https://urn.fi/URN:NBN:fi:aalto-202404113066

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[article-cris] Perustieteiden korkeakoulu / SCI