Contrastingly, the XGBoost algorithm's performance lagged behind the multi-condition model, and the inclusion of intermediate descriptors did not enhance results, resulting in marginal declines (details shown in Table showcase the potential application of our model in this field. We then used the corresponding models embedded with intermediate knowledge, which exhibited the best performance on the full novelty dataset, to predict the yields of these reactions. The results in Figure showed an R 2 of 0.45, MAE of 13%, RMSE of 15%, and MSE of 2%. Notably, the prediction errors for 18 reactions were less than 5% (as shown in Figure ). Considering the size of the training dataset (approximately 400), the performance of our model was quite strong, and its generalization ability would likely improve with additional data.