Dissolving the Periodic Table in Cubic Zirconia: Data
Mining to Discover Chemical Trends
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Abstract
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’ 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 identifygenerally by chemical intuitiona
simple, easily measured, or predicted <i>descriptor</i> 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–ranking–modeling (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’ stability when dissolved in zirconia