We trained our models using 95% of the remaining data in BigSolDB, reserving 5% for validation and model selection. To avoid data leaks, we split our data based on individual solubility experiments, one of which is visualized in Figure , which ensure no solutes appear in both training and validation sets. Since we split our dataset by experiment and solute (Figure ), we ensure that we rigorously test extrapolation. The performance of the trained solution fastprop model on the training and validation performance is shown in the parity plot in Figure . Performance is quantified via the Root Mean Squared Error (RMSE) and the Percentage of Predictions within 1 logS unit -a metric based on the upper reported limit of experimental reproducibilityreferred to as % log S ±1. The model achieves excellent interpolation accuracy, with RMSE = 0.22 and % log S ± 1 = 99.3%. Similarly, the optimized solution chemprop model achieved an RMSE = 0.28 and % log S ± 1 = 99.2%