We demonstrated the model's significant utility for novel mate-745 rials discovery in an exercise of identifying promising candidate 746 oxides in the context of various clean energy applications: solar 747 thermochemical water splitting and energy storage, CO 2 conver-748 sion, and SOFC cathodes and electrolytes. We screened struc-749 tures drawn from a different database (Materials Project) than 750 the source of the training structures (NRELMatDB) using the sim-751 plest, "Element-only" graph encoding strategy (i.e., requiring only 752 the host crystal structure as input). Narrowing down the ∼ 35, 000 753 oxides initially queried to as few as ∼ 10 depending on the strin-754 gency of down-selection criteria, we identify candidates exhibit-755 ing the greatest potential based on predicted vacancy defect en-756 thalpy and host oxide stability criteria, which also "rediscovers" 757 known materials from previous experimental literature. This ML 758 strategy therefore efficiently reveals a handful of top candidates 759 from an intractably large space for brute-force DFT or experi-760 ments, and will help facilitate the discovery of optimal materi-761 als in the future (along with significant potential for more chem-762 istry and structural diversity in the training data). Even stricter 763 down-selection can now additionally be explored based on more 764 detailed properties from first principles calculations that are only 765 tractable across a small number of materials. The final critical 766 contribution of this study is the prediction of defect formation 767 enthalpies across all sites to rapidly estimate defect densities at 768 finite temperatures. By accounting for configurational entropy 769 in high-throughput, we can assess material performance at finite 770 temperatures, rather than relying purely on a zero kelvin picture 771 from individual defect predictions across just a handful of mate-772 rials.