Therefore, a method of improving the accuracy of xTB-sTDA in a way that preserves its high-throughput characteristics is desired. One approach is to calibrate the results of xTB-sTDA against a higher-accuracy computational methods using machine learning (ML). This type of calibration from a baseline method to a reference method is known as the delta-ML or ∆-ML approach , and has been applied widely in the literature for various computational techniques. For example, it has been used for calibrating ground state energies and structures from semiempirical methods to DFT and coupled cluster (CC) accuracy, and calibrating DFT molecular dynamics simulations or potential energy surfaces to CC accuracy. Recently, the ∆-ML approach has been applied to calibrate excited state properties, for example calibrating photoemission spectra from DFT to G 0 W 0 , and calibrating excited state energies with TD-DFT against CC methods and against experiment. For xTB-sTDA, a few studies have used a linear calibration technique to correct excited state energies, instead of ML. However, the improvement from linear calibration was low, with a mean absolute error (MAE) of around 0.2 eV, compared to around 0.1 eV for the ML studies presented above. To the authors' knowledge, no previous study has applied ML to calibrate xTB-sTDA. Due to the promising potential of ML to increase the accuracy of baseline methods, this work presents a ML calibration of the excited state energy levels output by xTB-sTDA, with the motivation of more efficient exploration of excited state space. As mentioned previously, existing spectral conversion materials utilize excited states to up-or down-convert photon energy, but have low efficiency due to (a) energy level misalignment which leads to energy loss and (b) low absorptionto-emission probability. This study focuses on the first issue, making it easier to accurately predict energy levels and therefore design high-efficiency spectral conversion materials. The following sections present the methods and results of our excited state energy calibrations.