The accuracy of the GNN was first assessed on an external test set, revealing an overall excellent agreement with the explicit-solvent reference (see Figure in the Methods section). The largest deviations, which remain for the most polar and rigid solvents of our set (e.g., HMPA, glycerin, and octanol), can be rationalized considering their overall higher mean forces and sampling errors for the reference calculations. A key aspect of our GNN architecture (Figure ) is the simultaneous training of the entire solvent set. Briefly, a single model is trained for the entire set of solvents, and differences among solvents are captured through the embedding of solvent-specific feature vectors. This concurrent training should optimize information gain and efficiency by allowing the GNN to exploit transfer learning among solvents, i.e., to link similar solvent behavior to related features. To probe the GNN's ability to develop such an artificial "chemical intuition", we performed a principal component analysis (PCA) of the solvent feature vectors. In Figure , the three largest principal components, which together account for 47% of the variance, are shown. The resulting 3D arrangement of solvents groups chemically similar solvents (e.g., having the same functional group) together, in line with a chemist's intuition and going beyond a solvent classification based solely on dielectric permittivity. For example, THF and DMSO have very different dielectric permittivities but still appear close in the PCA projection as they are both polar aprotic solvents. On the other hand, solvents with similar dielectric permittivity but different functional groups are far apart in our projection (e.g.chloroform and acetic acid). This demonstrates that the GNNIS model does indeed identify solvent effects that are not accounted for in classical continuum-based implicit solvent models and supports our strategy to train multiple solvents in a joint manner.