In summary, we compared the effectiveness of two approaches for the solvent design case: PWAS and DoE-QM-CAMD. Our find-ings reveal the strengths in each method: the DoE-QM-CAMD approach, utilizing a MLR model, demonstrates more robust predictive capabilities across the entire design space; while, PWAS, employing PWA surrogates, can better predict reaction rates in proximity to optimal regions, which is important for our optimization objective. Furthermore, PWAS can learn correlations between solvent properties and reaction rates and offer valuable insights.