Adaptive cluster expansions and redox-dependent atomic ordering

• Published in: Computational materials science Vol. 83; pp. 207 - 211
• Main Authors: Dalach, P., Ellis, D.E., van de Walle, A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.02.2014; Elsevier
• Subjects: Atomic ordering; Cluster expansion; Condensed matter: structure, mechanical and thermal properties; Density Functional; Exact sciences and technology; Fuel cell; Inorganic compounds; Lanthanum perovskite; Monte Carlo; Physics; Redox; Structure of solids and liquids; crystallography; Structure of specific crystalline solids; Water, oxides, hydroxides, peroxides; Yttria-stabilized zirconia; Density Functional; Lanthanum perovskite; Monte Carlo; Atomic ordering; Yttria-stabilized zirconia; Redox; Cluster expansion; Fuel cell; Solid oxide fuel cells; Ground states; Interatomic interaction; Oxides; Perovskites; Effective charge; Monte Carlo methods; Ordering; Redox potential
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2013.10.013

•Adaptive cluster expansions (ACE) improve prediction of atomic ordering.•ACE allows unprecedented modeling control of the local redox environment.•Compact expansions permit physical interpretations of 2- and 3-body interactions.•The approach is tested for oxide-fuel cell materials YSZ and LSCR. 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 (La0.8, Sr0.2)(Cr0.8, Ru0.2)O2.9 (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 0K 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.