Accelerated phonon transport calculations for nanostructures: Combining neuroevolution potentials and compressed sensing

Abstract

Thermal management in nanostructured devices necessitates the accurate and efficient prediction of phonon transport properties. However, solving the Boltzmann transport equation via first-principles calculations is often computationally prohibitive due to the extensive supercells required to model realistic nanostructures. In this work, we present an accelerated, automated workflow that synergizes neuroevolution potentials with compressed sensing techniques to efficiently extract high-order anharmonic force constants. We validate this approach using silicon thin films as a prototype, explicitly accounting for the complexities of surface reconstruction. Our results demonstrate that this framework achieves accuracy comparable to density functional theory while reducing the computational cost by several orders of magnitude. The workflow successfully reproduces phonon dispersion relations and captures the temperature- and size-dependent trends of lattice thermal conductivity, incorporating the critical contribution of inter-mode coherence. This methodology offers a scalable and robust solution for the high-throughput thermal characterization of low-dimensional materials.