On the SolProp dataset, the Vermiere model performs slightly better (RMSE = 1.43 and % log S ± 1 = 66.9%), but still exhibits severe systematic bias, with several specific experiments appearing with severaly overpredicted temperatures gradients compared to parity (Figure ). In contrast, the solution fastprop and chemprop models perform significantly better, with RMSE = 0.83 and % log S ± 1 = 78.1% for fastprop and RMSE = 0.83 and % log S ± 1 = 76.1% for chemprop (Figure ). To go beyond these aggregate performance metrics, we also analyzed predictions on specific solutions in the SolProp test set, and observe that our models can correctly rank order solubility in different solvents, and can distinguish solubility in extremely similar solvents when the Vermiere model cannot (Section B).