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