Before considering external datasets, ML model performance was evaluated on subsets of the training sets themselves. 10-fold cross-validation (training on 90% of the data and testing on the remaining 10% 10 times with non-overlapping test sets) was conducted separately on the SCOP-PCQC, QM-symex-10k, SCOP-AL-Exp, and QM-symex datasets. Class 1 models (only molecular structure as input) and Class 2 models (both molecular structure and sTDA energy as input) were both tested. Results are compiled in Figure (a), with plots of Class 2 models for SCOP-AL-Exp and QM-symex shown in Figure (b)-(e). As seen, the original data has low accuracy when compared to TD-DFT results, and linear calibration improves the accuracy slightly. However, there is not a clear linear shift due to some groups of molecules located farther from the line of best fit. In contrast, for both datasets, the ML-calibrated values have much lower MAE and demonstrate significant improvements from uncalibrated xTB-sTDA values, especially for T 1 data. The increase in accuracy with ML is likely because ML detects higher-order patterns, allowing groups of molecules to shift locally instead of having to follow a global calibration rule.