Besides the radar charts, we also depict the structures of the solvents and their categorization based on the constituent functional groups in Fig. . Fig. arranges the solvents in the sequence of optimization steps, distinguishing the initial and subsequent active-learning samples with a black line in, while Fig. arranges the solvents into partitions, with orange lines denoting partition boundaries. By examining Figs. 12, 13 and 14, it can be observed that PWAS explores diverse solvent structures, covering large ranges of solvent properties, during the initial sample step, and then gradually converges toward clear patterns while maintaining an exploratory nature. These results demonstrate the effectiveness of PWAS at finding a diverse and promising set of solvents over the constrained mixed-integer and categorical domain. One major advantage of PWAS is that it allows one not only to group solvents with similar functional groups into the same partition, but also to place solvents with similar chemical properties into the same partition through the PARC mechanism. These similar findings across the functional group-and property-based design spaces highlight the adeptness of PWAS in identifying key implicit relationships, demonstrating its capability to effectively discern and utilize the links between functional groups and the ensuing solvent properties.