Given the strong correlations observed among the values of most QM descriptors calculated using different model chemistry, we hypothesize that the choice of DFT level used to obtain the QM descriptors considered in this study would have minimal impact on the performance of the QM-augmented model. Because performing QM calculations to derive descriptors for each new molecule can be expensive and time-consuming, we train ML models to predict these descriptors easily and efficiently. Herein, We train three models on different subsets of QM descriptors using ωB97XD/def2-SVP//GFN2-xTB as the model chemistry.We also tested using a single model co-trained on all descriptors, but this single model had worse performance than the three individual models ultimately used in this work. (Table ).