Finally, to show the generalizability of the methodology presented here, we choose a different reference technique beyond TD-DFT, namely CC2. CC2 is known to better predict excitation energies than TD-DFT, but its computational cost is often prohibitively expensive. We use the CC2 S 1 values compiled in QM8, randomly sampling 10k molecules as the training set and using the other 11.5k as the test set. xTB-sTDA values were generated using the same methodology as before. For ML model ensemble generation, because of the smaller dataset, we use 20-fold cross-validation with 95%/5% train/validation splits. This helps ensure all of the data is used in training. As a Class 2 model, both SMILES and sTDA energy are given as input. The new model is termed xTB-CC-ML to distinguish it from the previously generated xTB-ML models.