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 language | English (US) |
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Pages (from-to) | 207-211 |
Number of pages | 5 |
Journal | Computational Materials Science |
Volume | 83 |
DOIs | |
State | Published - 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