To prove this for the xTB-ML models generated in this work, we consider Class 1 ML models trained on the 20k and 300k datasets presented previously, but instead of being trained on the error between TD-DFT and xTB-sTDA (as xTB-ML-(20k,300k) are), the new models, called TDDFT-ML-20k,300k, are trained directly on TD-DFT values. (Note that direct Class 2 ML models would give equivalent results to calibration Class 2 models, since the sTDA energy is provided as input.) xTB-ML-(20k,300k) and TDDFT-ML-20k,300k are then tested on the MOPSSAM 143 dataset, with TDDFT-ML directly predicting values, and xTB-ML predicting the errors which are added to the sTDA energies to get the final cali-