Gaussian approximation potential for studying the thermal conductivity of silicene

Abstract

Due to the compatibility with the well-developed Si-based semiconductor technology, the properties of silicene and silicene-based materials have attracted tremendous attention. Among them, the thermal conductivity (TC) is of special importance for electronic devices. However, unlike graphene, the poor quality of empirical potentials hinders the reliable evaluation of TC for silicene using molecular dynamics (MD). Here, we present a Gaussian approximation potential (GAP) for silicene based on ab initio derived training data. The potential can precisely describe the geometries, mechanical properties, as well as phonon dispersion of free-standing sheet, outperforming any other empirical ones. Using sinusoidal approach-to-equilibrium MD simulations based on the GAP potential, the TC of silicene is found to be 32.4±2.9W/mK at room temperature. Importantly, our result achieves a good agreement with Boltzmann transport equation (BTE) based first-principles predictions (∼30W/mK), such that the TC value of silicene is confirmed via both MD and BTE; thus, we prove that the accuracy of machine learning potentials, like GAP, can enable a faithful prediction of TC at a density functional theory (DFT) level.