Two avenues of improvement were pursued -first, using a larger training set, and second, adding additional input data to the ML model. As discussed in the Methods section, a larger training set was generated using active learning to sample regions of chemical space not represented in the 20k training set. The expanded ML model uses 200k molecules chosen with active learning plus all QM-symex molecules (120k), added to the initial 20k training set, for a total of approximately 300k molecules. To distinguish the two ML models generated, the 20k model is named xTB-ML-20k while the expanded 300k model is xTB-ML-300k. The second improvement explicitly included the xTB-sTDA calculated energy as an input to the ML model. As discussed previously, for these models, called Class 2, the xTB-sTDA energy was concatenated to the generated molecular features during the training process.