In contrast, PWAS, similar to most surrogate-based optimization strategies , solves the problem by employing an activelearning technique with exploration capability. It systematically identifies optimal solvents for examination, effectively balancing the trade-off between exploring new possibilities for model improvement and exploiting known knowledge of reaction kinetics. As opposed to DoE-QM-CAMD, which assumes linear relationship between the expert-derived solvent properties and ln k, PWAS adopts PWA surrogates to represent the correlations between the functional groups within the designed solvent and ln k, where the relationship between solvent properties and ln k are learned implicitly. As discussed in Section 4, PWAS leverages PARC to fit the surrogates, where PARC first clusters samples in K init initial partitions. The initial partitions are then optimized by balancing between enhancing separability, which relies on similarities among different solvents (in this context, functional groups), and improving the predictability of the surrogate function within each partition, in this case, the input-output correlations. Here, the output (ln k) correlates with the properties of the designed solvent and therefore can be implicitly learned during surrogate fitting, offering insights not available when solely considering individual functional groups.