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

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.

Order-N calculations for thermoelectric power factor based on linear response theory

“…Namely, we can investigate the thermoelectric coefficients of micron-scale materials from an atomistic viewpoint. Note that theoretical evaluation of ZT requires the calculations of thermal conductivities κ e and κ ph [36,39,51], but these calculations are out of scope of this study.…”

“…However, most thermoelectric materials used in real devices have various concentrations of dopants and are polycrystalline. Thus, largescale calculation techniques are also desired [38,39].…”