Adaptive cluster expansions and redox-dependent atomic ordering

P. Dalach, Donald E Ellis*, A. Van De Walle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

An adaptive cluster expansion (ACE) methodology is presented which enables exploration of atomic ordering interactions in solids as a function of the redox environment. A previously developed cluster expansion methodology is augmented via inclusion of explicit effective charge dependence within the topological cluster basis. This augmentation produces an enhanced fit precision across a wide composition range and the ability to directly control the model's redox state during Monte Carlo system equilibrations. The approach is validated in applications to yttria-stabilized zirconia (YSZ) and the perovskite (La 0.8, Sr0.2)(Cr0.8, Ru0.2)O 2.9 (LSCR), where significant variability in atomic ordering is seen across redox space. A locally adaptive lattice Monte Carlo sampling, utilizing the ACE methodology, is developed and validated in applications to determine the 0 K ground state configurations of YSZ and LSCR supercells with varying redox conditions. These equilibrations have direct relevance to solid-oxide fuel cell applications, whose components are subject to widely varying redox environments. The superior convergence of ACE results in a smaller number of numerically significant expansion terms, not only speeding the analysis but also permitting a physical interpretation of their meaning.

Original languageEnglish (US)
Pages (from-to)207-211
Number of pages5
JournalComputational Materials Science
Volume83
DOIs
StatePublished - Feb 15 2014

Keywords

  • Atomic ordering
  • Cluster expansion
  • Density Functional
  • Fuel cell
  • Lanthanum perovskite
  • Monte Carlo
  • Redox
  • Yttria-stabilized zirconia

ASJC Scopus subject areas

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics

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