There are several avenues for future work. First is improving the ML model architecture. While Chemprop's MPNN outperformed other ML models, primarily due to its advanced featurization, only the 2D molecular structure and xTB-sTDA energy were provided as input. Since the 3D structure is known, including this information would likely improve performance. Another improvement to the ML workflow would be to conduct a more intensive conformer search. While OpenBabel's gen3D function includes a search for 200 conformers, these may not include the lowest energy conformer, thus reducing the accuracy of the xTB portion of the workflow. Using a conformer searching tool such as CREST would be more comprehensive, although the computation time added may detract from the high-throughput nature of the xTB-ML process. The ML model could also be expanded to calibrate several singlet states instead of just the first, similar to that in Kang et al. Beyond higher level first-principles data, the calibration models could be further extended to experimental data. However, this would be time-consuming due to the requirement of real-world measurements. There have been a few previous studies in calibrating TD-DFT against experimental values, 28,29 but these used only small experimental datasets. There is a potential here to apply techniques such as text mining to extract experimental excited state data from published papers, though the differences in reporting may make this difficult.