This study spans three types of models: those trained with the RIGR featurizer (RIGR), those trained with the native featurizer (Native), and those trained with the native featurizer alongside data augmentation (Native + Aug). Figure shows the D-MPNN architecture used for training the models. In this section, we first outline the data splitting methods, then describe the training and evaluation of models on these splits, and introduce new metrics to assess model performance in the context of resonance.