For the INDT and VerdeDB datasets, the results are less intuitive. In these cases, the Class 1 xTB-ML-20k ML model, i.e. the model trained on a smaller training set with less input data, performs better. There are several reasons this could be happening. The INDT and VerdeDB datasets are composed specifically of molecules relevant to photon conversion or green chemistry applications. Similarly, the xTB-ML-20k dataset is composed primarily of literature scraped molecules intended for spectral conversion applications, so it is more likely to include molecules similar to those in INDT or Verd-eDB. Although the training set size in xTB-ML-300k is larger, the method of expansion through active learning specifically includes molecules significantly different than the 20k training set, so the model may not increase in accuracy for photon conversion molecules, i.e. is not backwards compatible. In terms of why the Class 2 models perform worse than Class 1 models, this could be related to the nature of the training sets used. Both the 20k and 300k training sets have only a few low-excited state energy molecules. Therefore, the calibration in this region may be inaccurate. As seen in Figure , the INDT dataset is primarily composed of low-energy molecules. Therefore, if the energy is localized by providing the sTDA energy, the molecule may undergo an inaccurate calibration. This localization is minimized if the calibration is done solely based on molecular structure, allowing a more accurate calibration.