A machine learning interatomic potential for Ge-Te alloys

Germanium-Tellurium alloys are complex materials with a range of important applications. Computational study of these systems commonly uses density functional theory (DFT) which severely limits the size and time scales that can be probed. We present an interatomic potential for GeTe alloys based on the recent atomic cluster expansion (ACE) architecture, trained with representative configurations from DFT calculations spanning the entirety of the Ge_xTe_1-x system. This system contains multiple complex thermodynamic processes, most notably the fast and reversible phase-change in the 50:50 composition and negative thermal expansion in the tellurium-heavy liquid, which makes developing a stoichiometrically transferable potential challenging. By combining a refined exchange-correlation functional for the solid state with an explicit inclusion of dispersion interactions, we obtain an accurate description of the structure of elemental germanium, tellurium, and its alloys capable of simulations on the timescales of the phase change process. We report on its ability to reproduce the experimental thermodynamic properties and discuss the implications for past and future atomistic phase-change material simulations.