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

Konstantinos Konstantinou, Juraj Mavračić, Felix C. Mocanu, Stephen R. Elliott

Research output: Contribution to journalArticleScientificpeer-review


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.

Original languageEnglish
Article number2000416
Number of pages8
JournalPhysica Status Solidi (B) Basic Research
Issue number9
Early online date2020
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed


  • ab initio molecular dynamics
  • amorphous SbTe
  • density functional theory
  • Gaussian approximation potential
  • machine-learned potentials
  • phase-change-memory materials
  • thermoelectric materials

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics


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