Additionally, we found that the optimized conditions were often specific to each substrate, as demonstrated by cross-application of synthesis conditions (Figure ). Each substrate's highest yield was achieved under its uniquely optimized parameters, highlighting the necessity of a tailored approach rather than a one-size-fits-all methodology. This specificity is crucial, as conditions optimized for one substrate are different and did not necessarily translate to others for the optimal reaction yields (Table ). By dynamically adapting and optimizing reaction conditions, our approach reduces the trialand-error inherent in traditional methods, enhancing efficiency and productivity in chemical synthesis. The active learning framework proved effective in streamlining the optimization process, effectively reducing the experimental burden while achieving high yields with the active learning approach. , and the search space parameters are listed in Table .