Overall, these results show that the ML models significantly improve raw xTB-sTDA calculated values. In most cases, the best-performing ML model reduces the MAE by more then half. Further, while not shown in Figure , the ML models also consistently outperform linear calibration, showing the benefits of a higher-order calibration. We have thus shown that machine learned calibrations can help improve the accuracy of xTB-sTDA results over a wide variety of datasets, when compared to a TD-DFT (B3LYP) reference. We can now use these models for various applications. Test set (size) C. Applications of xTB-sTDA calibration 1. Direct vs. calibration ML models ML has been used extensively in the past to explore the excited state space of molecules, primarily being used to directly predict excited state properties such as energies, spectra, and dynamics. However, we expect a ML model trained to directly predict TD-DFT results to perform worse than a calibration model where the baseline method does most of the work and the calibrator simply shifts the result in the right direction. This calibration or ∆-ML approach has been used extensively in the past, and has shown superior performance to pure ML models. Calibration is particularly useful for improved out-of-domain predictive performance. Because supervised ML is a data-driven method, it may have poor performance on molecules distinctly different than those in the training set. In contrast, xTB-sTDA is data-agnostic, so it should give reasonable results regardless, and ML should slightly improve results through calibration.