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We propose an efficient approach for simultaneous prediction of thermal andelectronic transport properties in complex materials. Firstly, a highlyefficient machine-learned neuroevolution potential is trained using referencedata from quantum-mechanical density-functional theory calculations. Thistrained potential is then applied in large-scale molecular dynamicssimulations, enabling the generation of realistic structures and accuratecharacterization of thermal transport properties. In addition, moleculardynamics simulations of atoms and linear-scaling quantum transport calculationsof electrons are coupled to account for the electron-phonon scattering andother disorders that affect the charge carriers governing the electronictransport properties. We demonstrate the usefulness of this unified approach bystudying thermoelectric transport properties of a graphene antidot lattice.

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