Our "element-only" encoding models can perform consistent predictions on input host crystal structures in which the DFT settings for host relaxations are not identical to those used to create the training set (see Section S2 and Figure for details). Thus we can now efficiently predict ∆H d across 10,000s of DFT-relaxed crystal structures included in open source repositories beyond NRELMatDb, such as Materials Project (v2021.03.22). We employ the "Element-only" encoding model at the expense of slightly lower accuracy since Materials Project (MP) data doesn't necessarily contain all the features needed for the "Full" encoding models. Figure shows that the space of ∼35,000 oxides (excluding non-metals) is reduced to about ∼2,200 structures by setting a maximum energy above the hull requirement, E H < 0.1 eV/atom, and discarding any materials with cations not present in the training set. From these remaining ∼2,200 host oxides, we predict ∆ Ĥd K for the ∼48,000 symmetrically unique defect sites. Before proceeding with candidate down-selection, we perform yet another hold-out validation by comparing our MP screening predictions with existing first principles calculations and model predictions that could be easily mined from the literature (see Section ).