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

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

Tutkimustuotos: ArticleScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli2000416
Sivumäärä8
JulkaisuPhysica Status Solidi (B) Basic Research
Vuosikerta258
Numero9
Varhainen verkossa julkaisun päivämäärä2020
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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