To test the impact of training size on accuracy, 8 different ML models were generated with training sizes ranging from 100 to 15,000. The models were then predicted on the remaining molecules in QM8 not used in the training set. The MAE of the test set (against CC2 values) was compared to the MAE of PBE0/def2-TZVP and CAM-B3LYP/def2-TZVP, as shown in Figure ). As seen, a training size of less than 500 molecules allows xTB-sTDA to achieve similar accuracy to PBE0. It is more difficult to match CAM-B3LYP, but this is achieved at a training size of around 1500. At the largest training size considered (15k), xTB-CC-ML vastly outperforms both TD-DFT techniques, with a 62% lower MAE compared to PBE0 and 47% lower MAE compared to CAM-B3LYP. These are promising results; however, the xTB-CC-ML model may not be as generalizable as xTB-ML, due to the smaller (10k), less diverse (only small molecules with up to 8 heavy atoms) training set. To further explore generalizability, two additional ML approaches were considered. First we calibrate xTB-sTDA against CC2 with transfer learning. The learning rate analysis above showed a 10k training set size gives high accuracy while leaving enough molecules in the test set for a reasonable error measurement. Therefore, we train a ML model on 10k randomly sampled molecules from QM8 to predict CC2 values given SMILES and xTB-sTDA energy as input, using the Class 2 xTB-ML-300k model as a starting point, with the first MPNN and first FFNN layers frozen. We use the largest, most detailed ML model considered in this work as a starting point so any adjustments made to this model using the smaller QM8 set should propagate to the larger model. The results of this analysis on the 11k test molecules are shown in Figure . We call this type of model xTB-CC-TL. Our second approach was to calibrate xTB-sTDA against B3LYP, then calibrate B3LYP against CC2. We first generate an ML model that calibrates B3LYP against CC2. We ran B3LYP independently on QM8 using the same settings as outlined in the work. Using these values, we train an ML model on 10k molecules in QM8 that takes in SMILES and B3LYP energy as input and predicts CC2 energy, called B3LYP-CC2-ML. We then apply the Class 2 xTB-ML-300k model generated in this work to predict B3LYP energies. We finally use these predictions as an input to the B3LYP-CC2-ML model to get CC2 energies. We therefore calibrate xTB-sTDA to B3LYP first, and then to CC2. This overall approach is called xTB-B3LYP-CC-ML. The results of this calibration are shown in Figure .