To test broad applicability, a more general ML model is needed. Therefore, an overarching ML model was trained on the 10k SCOP-PCQC molecules combined with the 10k QM-symex-10k molecules, for a total training size of 20k molecules. The overarching ML model was first tested on the MOPSSAM 143 external dataset. As seen in Figure , the ML calibrated xTB-sTDA data matches TD-DFT values better than both the original data and the linearly calibrated data. While the data are sparse, there are a few regions where the improvement is clearly visible. For example, for high S 1 energies, the linear calibration tends to overcorrect, while for low S 1 energies the linear calibration undercorrects. In contrast, the ML model is more flexible and adequately corrects models in both regions. For T 1 energies, the ML model performs similarly to linear calibration with both MAE and R 2 metrics. This is likely because xTB-sTDA nearly always over-predicts the T 1 energy, so calibrating it only requires shifting in one direction, which makes linear calibration sufficient for the task. For S 1 energies, there are both instances of over-and underprediction, which motivates the need for an ML model. However, there is clearly room for improvement in these results, as the ML MAE is still high.