In the model training step, ML models were trained to take input data and predict xTB-sTDA vs. TD-DFT error. Two classes of models were trained, one with only the SMILES string as input (Class 1) and another with both SMILES and sTDA energy as input (Class 2). During training, the SMILES string was converted to the graphical representation of the molecule, which was then featurized using an MPNN. If the sTDA energy was used (Class 2), it was concatenated as an extra feature at this step. Then, feature to property prediction was conducted using a feed-forward NN. To improve reliability of results and ensure all molecules were included in the training process, a 10-model ensemble was generated with 10-fold cross-validation using 80%/10%/10% train/validation/test splits. This process resulted in an optimized ensemble ML model for error prediction.