Optimizing thermoelectric performance of graphene antidot lattices via quantum transport and machine-learning molecular dynamics simulations

Thermoelectric materials, which can convert waste heat to electricity or be utilized as solid-state coolers, hold promise for sustainable energy applications. However, optimizing thermoelectric performance remains a significant challenge due to the complex interplay between electronic and thermal transport properties. In this work, we systematically optimize $ZT$ in graphene antidot lattices (GALs), nanostructured graphene sheets with periodic nanopores characterized by two geometric parameters: the hexagonal unit cell side length $L$ and the antidot radius $R$. The lattice thermal conductivity is determined through machine-learned potential-driven molecular dynamics (MD) simulations, while electronic transport properties are computed using linear-scaling quantum transport in combination with MD trajectories based on a bond-length-dependent tight-binding model. This method is able to account for electron-phonon scattering, allowing access to diffusive transport in large-scale systems, overcoming limitations of previous methods based on a nonequilibrium Green's function formalism. Our results show that the introduction of the antidots effectively decouples lattice and electronic transport and leads to a favorable and significant violation of the Wiedemann-Franz law. We find that optimal $ZT$ values occur in GALs with intermediate $L$ and $R$, closely correlated with peak power factor values. Notably, thermoelectric performance peaks near room temperature, with maximal $ZT$ values approaching 2, highlighting GALs as promising candidates for high-performance thermoelectric energy conversion.