After evaluating the performance of xTB-ML, we then used the model for four applications. First was comparing the xTB-ML model against directly predicted energies with ML, showing the xTB-ML model had better accuracy (0.11 eV vs. 0.21 eV MAE). Second was rapidly screening 250k molecules for suitability as sensitizers and emitters for spectral conversion applications. Third was mapping inaccuracies of xTB-sTDA in chemical space, using the ML model to predict errors. This was used to see which regions of chemical space xTB-sTDA has high errors in. S 1 errors were small, with most molecules being within 0.5 eV. There were clear regions where xTB-sTDA overpredicted S 1 , but only a few for underprediction. T 1 energies were generally overpredicted, with most molecules being between 0 and 1 eV below TD-DFT values. Global chemical space mapping provides another method of predicting xTB-sTDA error, by calculating which cluster a molecule belongs to and referencing the MAE of that cluster. Properties of low-error molecules were also evaluated, finding non-aromatic molecules are likely to have higher error. The final application was generalizing the methodology to calibrate xTB-sTDA against coupled cluster theory, and generating a new xTB-CC-ML model. The calibrated xTB-CC-ML values had high accuracy (0.10 eV MAE), out-performing TD-DFT values calculated with PBE0 (0.26 eV MAE) and CAM-B3LYP (0.19 eV MAE). We also generated more general calibration models with transfer learning and using B3LYP as an intermediary.