To further analyze the exploitation and exploration capabilities of PWAS, we examine the solvents determined by PWAS. Specifically, we aim to show that PWAS can exploit the surrogate to implicitly learn the preferred solvent properties that lead to a high reaction rate, and can explore the design space to obtain a set of solvents with diverse structures and chemical properties. Before examining specific solvents, we first perform a sensitivity analysis using Partial Dependence Plot (PDP) and Individual Conditional Expectation (ICE) plots to investigate the relative influence of solvent properties on the reaction rate constant, which we will then use to assess PWAS's exploitation and exploration capability. Seven representative descriptors considered are refractive index at 298K (n 2 ), Abraham's overall hydrogen-bond acidity (A), Abraham's overall hydrogen-bond basicity (B), dielectric con- stant at 298K (ε), microscopic surface tension at 298 K (γ), aromaticity, and halogenticity, which are used in a successful quantum mechanical continuum solvation model . The descriptors of all feasible solvents are calculated using the group contribution method of Sheldon et al. . As can be seen in Fig. , the fluctuation in PDPs is most pronounced for ε, while the fluctuation in ICEs is most significant for three properties, namely, n 2 , B, and ε, indicating their strong marginal effect on predicting ln k. This finding aligns with the established results for S N 2 reactions.