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

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 identifygenerally by chemical intuitiona 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

Robust FCC solute diffusion predictions from ab-initio machine learning methods - Supplementary material

We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We sep- arately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.<br