In contrast, our optimized solution fastprop and solution chemprop models exhibit much more consistent performance between the Leeds and SolProp test sets, highlighting the strong performance of our models under rigorous solute extrapolation (Figure ). The slightly decreased accuracy of our models on the Leeds test set is perhaps attributable to the increased solute diversity in the Leeds dataset, which is to be expected given the difference in its construction. Both models also exhibit accurate and physically-realistic predictions of the gradient on the solubility with respect to temperature, indicating the models learned physically-realistic temperature dependence -critically important in specific process chemistry applications. The training data size study and the error analysis presented in Figure shows that our optimized models, which rely on distinct molecular mappings, both converge to the same performance limits with similar distributions of predictions and errors. The highly correlated predictions and cumulative residuals show both models achieve not only the same average performance, but predict similarly across the tests sets. chemprop should be able to continuously learn a better representation as the size of the training set increases, as demonstrated by Heid et al. on several molecular properties. However, our optimized solution chemprop model performance stops improving after a relatively small amount of training data, indicating that variability in the training data limits the model's ability to learn an improved representation and predict more accurately.