Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends

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• 作者：Bryce Meredig ; C. Wolverton
• 刊名：Chemistry of Materials
• 出版年：2014
• 出版时间：March 25, 2014
• 年：2014
• 卷：26
• 期：6
• 页码：1985-1991
• 全文大小：471K
• 年卷期：v.26,no.6(March 25, 2014)
• ISSN：1520-5002

文摘

Doped zirconias comprise a chemically diverse, technologically important class of materials used in catalysis, energy generation, and other key applications. The thermodynamics of zirconia doping, though extremely important to tuning these materials鈥?properties, remains poorly understood. We address this issue by performing hundreds of very large-scale density functional theory defect calculations on doped cubic zirconia systems and elucidate the dilute-limit stability of essentially all interesting cations on the cubic zirconia lattice. Although this comprehensive thermodynamics database is useful in its own right, it raises the question: what forces mechanistically drive dopant stability in zirconia? A standard tactic to answering such questions is to identify鈥攇enerally by chemical intuition鈥攁 simple, easily measured, or predicted descriptor property, such as boiling point, bulk modulus, or density, that strongly correlates with a more complex target quantity (in this case, dopant stability). Thus, descriptors often provide important clues about the underlying chemistry of real-world systems. Here, we create an automated methodology, which we call clustering鈥搑anking鈥搈odeling (CRM), for discovering robust chemical descriptors within large property databases and apply CRM to zirconia dopant stability. CRM, which is a general method and operates on both experimental and computational data, identifies electronic structure features of dopant oxides that strongly predict those oxides鈥?stability when dissolved in zirconia.