Figure (a) shows the results of comparison for S 1 and T 1 energies. As seen, all ML models vastly outperform the linear calibration method. Between the ML models, CP MPNN performs the best for both T 1 and S 1 . Note that the large variability in R 2 can be explained by the presence of outliers in the test set -since the test set was only composed of 100 molecules (10% of 1k), a few outliers can vastly impact performance. calibrated xTB-sTDA data for (a) S 1 and (b) T 1 energies, with test data from all 10 folds compiled and with outliers removed (full plots with outliers available in SI Section IV). From this analysis, it is evident that CP MPNN performs well in calibrating xTB-sTDA results, even with its default settings.