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Meeting Materials Science & Technology 2020
Symposium Ceramics and Glasses Simulations and Machine Learning
Presentation Title Defect Formation and Self-diffusion in Alumina: Computational Approaches
Author(s) Andy Paul Chen, Michael Finnis, Arthur Heuer
On-Site Speaker (Planned) Andy Paul Chen
Abstract Scope The introduction of minute concentrations of dopants in alumina (α-Al2O3) has shown to affect oxygen and aluminum self-diffusion rates significantly. This process has demonstrated remarkable utility in the case of high-temperature aluminum-rich Fe-based and Ni-based alloys, where the introduction of reactive elements (Y, Hf, and Zr, among others) suppresses the rate of growth of alumina scales during oxidation, thereby improving oxidation resistance of the alloy. The mechanism linking dopant concentration and self-diffusion rates, however, is poorly understood in alumina, and experimental figures of vacancy concentrations remain incommensurate with computational results for both polycrystalline and single-crystal frameworks (as in the “Corundum Conundrum”). In this study, we review established theory and recently-developed computational methods for vacancy analysis, and test the hypothesis that the use of density functional theory-Hartree-Fock (DFT-HF) hybrid functionals might be able to bridge the gap we currently see between theory and experiment.
Proceedings Inclusion? Undecided


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Defect Formation and Self-diffusion in Alumina: Computational Approaches
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