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The oxygen vacancy formation energy ($\Delta E_{vf}$) governs defect dynamicsand is a useful metric to perform materials selection for a variety ofapplications. However, density functional theory (DFT) calculations of $\DeltaE_{vf}$ come at a greater computational cost than the typical bulk calculationsavailable in materials databases due to the involvement of multiplevacancy-containing supercells. As a result, available repositories of directcalculations of $\Delta E_{vf}$ remain relatively scarce, and the developmentof machine learning models capable of delivering accurate predictions is ofinterest. In the present, work we address both such points. We first report theresults of new high-throughput DFT calculations of oxygen vacancy formationenergies of the different unique oxygen sites in over 1000 different oxidematerials, which together form the largest dataset of directly computed oxygenvacancy formation energies to date, to our knowledge. We then utilize theresulting dataset of $\sim$2500 $\Delta E_{vf}$ values to train random forestmodels with different sets of features, examining both novel featuresintroduced in this work and ones previously employed in the literature. Wedemonstrate the benefits of including features that contain informationspecific to the vacancy site and account for both cation identity and oxidationstate, and achieve a mean absolute error upon prediction of $\sim$0.3 eV/O,which is comparable to the accuracy observed upon comparison of DFTcomputations of oxygen vacancy formation energy and experimental results.Finally, we demonstrate the predictive power of the developed models in thesearch for new compounds for solar-thermochemical water-splitting applications,finding over 250 new AA$^{\prime}$BB$^{\prime}$O$_6$ double perovskitecandidates.

Oxygen Vacancy Formation Energy in Metal Oxides: High Throughput Computational Studies and Machine Learning Predictions