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

Abstract

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.