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

TL;DR

This paper introduces a combined machine learning and quantum transport approach to efficiently predict thermal and electronic transport properties in complex materials, validated on a graphene antidot lattice.

Contribution

It presents a novel integrated method coupling machine-learned potentials, molecular dynamics, and linear-scaling quantum transport calculations for comprehensive material property analysis.

Findings

Accurate thermal transport properties from large-scale MD simulations.

Effective modeling of electron-phonon interactions in complex materials.

Application demonstrated on thermoelectric properties of graphene antidot lattice.

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 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.