For S 1 , the linear calibration is minimal. The ML calibration remains centered around the raw data for mid-to highenergies, but changes more drastically at low energies. This mirrors the previous discussion of Figure , where the linear calibration either over-or under-corrects, but the ML model is more flexible. For T 1 calibration, at mid-to high-energies, both linear and ML calibration shift the energy down, reflecting the tendency of xTB-sTDA to consistently overestimate T 1 energies. For low T 1 energies, the ML model increases the raw energy, suggesting sTDA tends to sometimes spuriously calculate low T 1 energies which can be corrected with ML. Note that because TD-DFT data was not calculated for these 250k molecules, we cannot compare the calibration to ground truth, but based on the metrics presented in Figure , it is likely the ML-calibrated values are more accurate. Now that we have both S 1 and T 1 energies calculated for 250k molecules with xTB-ML, we can identify potential sensitizers and emitters, using the FOMs defined in Equations 1 and 2. Figure ) shows the results of screening molecules for potential sensitizers and emitters.