To predict QM descriptors on the fly, three separate D-MPNN models are trained on three subsets of QM descriptors computed using ωB97XD/def2-SVP: one for atom and bond properties, another for molecular properties of dipole and quadrupole moments, and the last for energy gaps, IP, and EA. For the model trained on atom/bond properties, the summation constraint for partial charges is set as the net charge for each species. Additionally, the nucleophilic/electrophilic Parr functions have a summation constraint of 1, while there are no constraints for other atom/bond descriptors. We split the dataset of 64,921 data points into 80% training, 10% validation, and 10% test splits for hyperparameter search with 30