Thermal Transport Simulation of Strongly Anharmonic GeSe through Machine Learning

First-principles calculation of lattice thermal conductivity of crystals with strong anharmonicity remains one of the critical challenges in materials science. Strong interactions between phonons resulting from the anharmonicity in the potential energy raise doubts about the adequacy of solving linearized Boltzmann transport equation with perturbations up to third order only. However, many popular thermoelectric materials belong to this class due to the practical demands for real-life applications. Recently, GeSe was predicted to exhibit ultralow thermal conductivity, even lower than the current state-of-the-art SnSe. In this work, we adopted compressive sensing, a machine learning technique in information theory, to obtain high-order force constants from a small amount of training data. To calculate the lattice thermal conductivity, molecular dynamics simulations which account for anharmonic effects to all orders were performed. Our results verified that optical phonons indeed make significant contributions to the thermal transport in GeSe. Furthermore, the lattice thermal conductivity deviates severely from the other theoretical prediction that considered up to third order only, suggesting that the inclusion of higher-order force constants would have a great impact on the accuracy of thermal conductivity calculation.