Thermal conductivity of GeTe crystals based on machine learning potentials

GeTe has attracted extensive research interest for thermoelectric applications. In this paper, we first train a neuro-evolution potential (NEP) based on a dataset constructed by ab initio molecular dynamics, with the Gaussian approximation potential (GAP) as a reference. The phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results, with the GAP potential having higher accuracy. Next, the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials, and both of them are in good agreement with the experimental results; however, the calculation speed when using the NEP potential is about 500 times faster than when using the GAP potential. Finally, the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP potential. The lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic effect. This study provides a theoretical tool for the study of the thermal conductivity of GeTe.