Efficient Modelling of Ge₁₅Te₈₅ using Active Learning Methods

Germanium-Telluride is a phase-change material (PCM) that shows promise for potential applications in advanced memory materials. In the 15:85 composition, several anomalous features, including a sharp density maximum and a likely fragile-to-strong transition in the dynamics, occur upon cooling. Unfortunately, accurate simulations of PCM materials typically rely on Density Functional Theory (DFT) and are very limited in the accessible size and time scales, making it difficult to model the properties of these materials. To overcome this challenge, we utilize recently developed machine-learning interatomic potentials (MLIPs) that enable the creation of lightweight and efficient potentials. These potentials are trained on and reproduce DFT-accurate descriptions of materials over a broad range of the phase diagram. We discuss active learning, compare training methods, and evaluate the ability of trained MLIPs to match experimentally known quantities of Ge₁₅Te₈₅. In particular, we find that these potentials reproduce the experimentally known structure to a high degree of accuracy.