The training curve (Figure ) was examined to assess whether the model had sufficient training data or required additional data. As the amount of training data increased, the error converged to 0.25, indicating that no further data was necessary for training the model, given the descriptors available and the complexity of the model being fixed. However, it is conceivable to train a similar model using fewer data (50% instead of 80% of the total data could probably suffice to train a similar model). Furthermore, to prove that this good accuracy does not correspond to a fortuitous random train/test split, a cross-validation evaluation was performed on the whole dataset using a 5-fold cross-validation scheme. The average error (RMSE) on the five validation sets equals 0.26 with a standard deviation of 0.01, which is very similar to the one obtained with the specific train/test split we obtained here. .