We developed an automated workflow for DFT vacancy defect 117 calculations (Figure ) as follows: (1) import the stoichiomet-118 ric host oxide crystal structures from the ICSD 50 that are avail-119 able in NRELMatDB; (2) relax the host supercell in ferromagnetic 120 and different possible anti-ferromagnetic spin configurations; (3) 121 choose the minimum energy host atomic and magnetic structure; 122 and (4) perform point defect calculations on the supercell using 123 the automated defect framework. In the data acquisition step, 124 we extract host properties (from step 3) and calculate vacancy 125 formation energies (after step 4), which then supplies input and 126 target properties, respectively, for training the machine learning 127 model. Further details on the DFT settings are provided in Sec-128 tion S1. DFT is currently the preferred method for high-129 throughput supercell defect calculations. True benchmark calcu-130 lations require total-energy methods beyond DFT, such as quan-131 tum Monte Carlo or the random phase approximation, which are 132 currently available only for few defect systems and with restric-133 tions in cell size and atomic relaxations. . Given the DFT-134 relaxed crystal structure, C h , of the host oxide with total energy 135 E h and the DFT-relaxed defected structure, C d , with total energy 136 E d (calculated using the standard supercell approach of Ref. 61), 137 we compute the enthalpy of defect formation via