These results indicated that our model can indeed recommend appropriate reaction conditions for some reactions, helping chemists to synthesize corresponding amide compounds with higher yields. to some extent, achieving an R 2 of 0.42, MAE of 12%, and RMSE of 16%. Isayev disclosed that reactivity cliffs were a reason for the poor performance of the model. Reactions were considered "cliffs" when their similarity surpassed 0.9, yet the yield difference was greater than 30. We were curious about whether our model's performance was affected by reactivity cliffs. Therefore, we predicted the yield of reactions identified as reactivity cliffs in Isayev's work. The prediction error averaged 0.34, indicating the reactivity cliff may also weaken the performance of our model. Although the performance of BERT in regression was not optimal, the model could extrapolate which reaction from a reaction pair with a reactivity cliff would achieve a greater yield, with a classification accuracy of 0.73 (details on prediction results, please see the Table in SI). To our delight, the model provided very satisfactory prediction results for reaction pairs that best fit the concept of reactivity cliffs (Figure ).