Predicted S 1 (eV) Predicted S 1 (eV) As seen, the performance of the directly trained ML model is worse than the ML-calibrated xTB data for both dataset sizes. The TDDFT-ML-20k model performs similarly to the linear calibration model (seen in Figure ), while the xTB-ML-20k model already significantly outperforms both. However, it is well known that direct ML models often require more training data than calibration ML models. When expanding the training set to 300k, the TDDFT-ML-300k model outperforms linear calibration but still underperforms compared to both xTB-ML-20k and xTB-ML-300k. Thus, calibrating xTB with ML gives much higher accuracy than using ML to directly predict energies. The benefit to a direct ML model is computational speed, as it can screen approximately 2 orders of magnitude more molecules in a given time period than xTB-ML. However, our goal is to attain approximately the same accuracy as TD-DFT methods, so a direct ML model would not be useful. From the above analysis, our assumption of the improved performance of a calibration ML model is upheld. We now apply our generated calibration ML models for high-throughput screening and chemical space mapping.