Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies

We report that the single interatomic potential, developed using Gaussian regression of data from density functional theory calculations, has high accuracy and flexibility to describe phonon transport with ab initio accuracy in two different atomistic configurations: perfect crystalline Si and crystalline Si with vacancies. The high accuracies of second- and third-order force constants from the Gaussian approximation potential (GAP) are demonstrated with phonon dispersion, Gruneisen parameter, three-phonon scattering rate, phonon-vacancy scattering rate, and thermal conductivity, all of which are very close to the results from density functional theory calculations. We also show that the widely used empirical potentials (Stillinger-Weber and Tersoff) produce much larger errors compared to the GAP. The computational cost of GAP is higher than the two empirical potentials, but five orders of magnitude lower than density functional theory calculations. Our work shows that GAP can provide a new opportunity for studying phonon transport in partially disordered crystalline phases with the high predictive power of ab initio calculation but at a feasible computational cost.