The type and architecture of the ML model must be optimized for performance. The 3 ML models considered were DeepChem's graph convolutional network (DC GCN), DeepChem's message passing neural network (DC MPNN), and Chemprop's directed message passing neural network (CP MPNN). These 3 models were chosen as they are commonly used graph neural networks, which have emerged as a natural choice for molecules where nodes represent atoms and edges represent bonds. The models each use different architectures and methodologies for featurization and property prediction. DeepChem's GCN is based on Duvenaud et al.'s paper which introduced a method to generalize conventional circular fingerprints using convolutional neural networks to generate neural graph fingerprints. DeepChem's MPNN is based on Gilmer et al.'s work which expands upon Duvenaud et al.'s GCN and is better able to identify correlations between node and edge states. Chemprop's MPNN is based on Yang et al.'s work which adds directionality to the message passing step, preventing noisy graph representations. We are interested in comparing each model's performance in our application. The default, out-of-the-box settings for each ML model were used, as described in SI Section III. Calibration of the 1000 molecules in the VerdeDB database was used to compare the different ML models. The small size and relatively homogeneous nature of this dataset makes it suitable for quickly comparing different ML models. Only the SMILES (simplified molecular-input line-entry system ) representation of the molecule was provided as input, and the goal of each model was to accurately predict the S 1 and T 1 error between xTB-sTDA and TD-DFT. Instead of predicting both S 1 and T 1 error simultaneously, two separate single-task models were generated, both using 10-fold cross-validation. For each fold, the trained ML model was used to predict error values of the test set. Then, each molecule's predicted error was added to the xTB-sTDA output to give a calibrated energy, called the xTB-ML value. The xTB-ML values were compared to the TD-DFT reference results by calculating an R 2 score.