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.Comment: 8 pages, 4 figure