To see if the performance could be boosted further, various architectural improvements were attempted. These included increasing the number of epochs (number of iterations to optimize the neural network weights) to 100, conducting hyperparameter optimization (finding the best ML architecture i.e. hidden size, depth, dropout, and number of feed-forward layers), and conducting multi-task training (using a single model to predict both S 1 and T 1 energies simultaneously). More details about these optimization approaches are available in SI Section III.