Machine learning potentials in studying phononic and thermal properties of germanium telluride

Germanium telluride (GeTe) is an important functional material that has been attracting extensive research attention. Challenges such as phase transition processes and crystallization of amorphous GeTe cannot yet be accurately calculated using ab initio molecular dynamics because of the time limitations of density functional theory calculations. Molecular dynamics simulation using empirical potentials can address the aforementioned issues. However, their accuracy relies on the validity of the empirical interatomic potential. With the advancement of computational methodologies within materials science and engineering, machine learning potentials (MLPs) have garnered substantial interest. In this paper, we review the applications of MLPs, including neural network potential, Gaussian approximation potentials, and neuroevolution potential, in studying the phonon properties of GeTe. Our focus includes the crystallization of amorphous GeTe, the mechanisms underlying structural phase transitions, and thermal conductivity. These advancements can offer valuable guidance for the utilization of GeTe in advanced thermal management and contribute to the exploration of MLPs in phonon physics.