K-Means clustering is then used to group the molecules into 18 clusters, with each cluster assigned to one of the training, validation, and test sets to achieve an approximate 80:10:10 split. A detailed description for the method used for generating extrapolative cluster splits is provided in Section S2 of the SI. The clusters are randomly shuffled between the training and validation sets five times while keeping the test set constant to generate five splits. For models trained on datasets augmented with resonance structures, the training, validation, and test sets are augmented independently to prevent data leakage. We assigned weights in the augmented training set that are inversely proportional to the number of resonance structures, ensuring that molecules with more resonance structures are not unfairly prioritized.