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

K Konstantinou and J Mavracic and FC Mocanu and SR Elliott, PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS, 258, 2000416 (2021).

DOI: 10.1002/pssb.202000416

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(N-3) cubic scaling with model system size, N. They are limited to a maximum model size of N approximate to 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- changememory 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 endmember of the GST compositional tie-line GeTe-Sb2Te3. The GAP-model results are compared with those obtained from conventional DFT-based AIMD simulations.

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