In summary, the direct ML model's performance strongly depends on the class of solutein some cases, model performance is comparable to the cluster-continuum and COSMO-RS solvation models (see SI for errors by anion type). However, the unsatisfactory ability of the model to extrapolate precludes it from modeling more general solvation phenomena. We therefore suggest using the model only for small organic acids, or in scenarios where several kcal mol -1 error are tolerable; for instance, ranking relative energies or in screening candidate compounds. Despite the model's apparent shortcomings, it still provides a very fast way of approximating ∆G * solv (A -). Finetuning on additional data may improve the performance of future models. In all cases except for in ethanol, the composite prediction model performs worse than the direct prediction model. For water, a RMSE of 4.15 kcal mol -1 is observed, which is higher than the 3.61 kcal mol -1 achieved by the direct prediction model. The errors are high across all solvents, but for the most part follow the ranking of accuracies as seen for direct prediction method (Figure ). One benefit of this method is that the sources of error can be examined. We examined the difference between the individual model predictions and the data. 1 kcal mol -1 in the original manuscript. These high errors are consistent with our benchmarks that showed high deviation (1.09 / 1.57 kcal mol -1 ) between D2A-dGsolv-neutral and dGsolvDB3/overlap.