quired input is the non-defected, DFT-relaxed host structure, and the model output is the predicted relaxed vacancy formation enthalpy of any site in the structure. The surrogate model therefore replaces the need to do an expensive DFT supercell relaxation for each symmetrically nonequivalent atomic site in the host, of which there can be a sizable number (up to about 100 in the present work) in compositionally and structurally complex materials. Other quantities typically derived from a DFT-relaxed structure like band gap (E g ), effective electron mass (m * ), compound formation enthalpy (∆H f ), and oxidation state (s) can readily be encoded into the material's graph if available to further improve prediction accuracy. Existing computational databases containing 10,000s of compounds with potentially up to millions of nonequivalent defect sites can be screened in an automated fashion using desktop computational resources with a prediction accuracy limited mainly by the amount and diversity of the vacancy defect training data.