Our final DFT training database consists of ∼1500 unique de-145 fect sites from ∼200 parent oxides, which span 15 cations (Mg, 146 Al, Ca, Ti, Mn, Fe, Co, Ni, Sr, Y, Nb, In, Ba, La, Ce), 63 space 147 groups, and 51 unique stoichiometries. The choice of chemi-148 cal space (Figure ) is motivated by previous literature, 58,63-65 149 which provides guidance on cations that form stable oxides and 150 play an active role in tuning oxygen vacancy formation energy 151 via their redox activity. Previous STCH material searches have 152 targeted perovskite (ABO 3 ) stoichiometry and related structures 153 systems (cubic, orthorhombic, tetragonal). Our training data is 154 more diverse and spreads across all seven crystal structure sys-155 tems and includes a wide range of stoichiometries to cover dif-156 ferent coordination environments and metal ion oxidation states 157 varying from 2+ to 5+. that will drastically reduce the computational cost associated with Requiring only C h and the index of the atom to be defected, i , as an input, the model negates the cost of a DFT supercell relaxation for each unique symmetry site when predicting ∆ Ĥd , and executing the ML screening is of negligible computational cost when querying C h from existing repositories like MP or NRELMatDB. The inputs for model training (the host's relaxed POSCAR file, formation enthalpy, bandgap, effective electron mass, and oxidation states) and neutral vacancy formation energies (for both oxygen and cations) are provided in our open-source data repository. Our dGNN closely follows the original Crystal Graph Convolutional Neural Network (CGCNN) method of Ref. 31, for which we highlight the necessary modifications to predict defect formation enthalpies. The deep learning framework is composed of three major steps.