Simulation of Phase-Change-Memory and Thermoelectric Materials using Machine-Learned Interatomic Potentials: Sb₂Te₃

Density-functional-theory (DFT)-based, ab initio molecular dynamics (AIMD) simulations of amorphous materials generally suffer from three computer-resource-related limitations due to their O(N³) cubic scaling with model system size, N. They are limited to a maximum model size of N ≈500 atoms; they are limited to time scales <1 ns; and, usually, only a single model can be simulated in any one investigation. This article discusses a machine-learned, linear-scaling (O(N)), DFT-accurate interatomic potential (a Gaussian approximation potential, GAP), originally developed by Mocanu et al. [J. Phys. Chem. B 2018, 122, 8998] using a Gaussian process regression method for the ternary phase-change-memory material Ge₂Sb₂Te₅ (GST). The chemical transferability of this GAP potential is explored in an application to the case of simulating amorphous models of the phase-change-memory and thermoelectric material Sb₂Te₃, an end-member of the GST compositional tie-line GeTe–Sb₂Te₃. The GAP-model results are compared with those obtained from conventional DFT-based AIMD simulations.