We first compare the models trained with RIGR and native featurizers on the unaugmented test set to gauge the impact of feature reduction on model performance for both interpolative and extrapolative tasks. Root mean square error (RMSE) is used as the evaluation metric as it penalizes the outliers to give a realistic estimate of model performance. Figure presents the results for the K-Means split, demonstrating that there is no performance loss; fact, RIGR consistently outperforms Native across all subsets of the test set, including species that do not exhibit resonance. The paired t-test confirms that the difference in performance between models is statistically significant (RMSE and p-values tabulated in Table ). The trends are identical for the random split, with similar test RMSE indicating that the model trained using RIGR generalizes well. For a comprehensive comparison between the RIGR, Native, and Native + Aug models and to quantify the effect of resonance on model performance, we tested these models on a test set augmented with multiple resonance forms. In addition to RMSE, we used RMSRR and RMSMRD (described in Section 2.3) metrics to assess both resonance variance and overall model performance. Figure shows the performance on the K-Means split, and the trends are similar for the random split (results tabulated in Table ).