In this part, we trained a model on three Suzuki-Miyaura coupling reaction datasets collected from HTE, USPTO, and Reaxys, with 5760, 7926, 6730 reactions suitable for training, respectively after data cleaning (the details could be found in SI, Section S7). The first step of catalytic cycle of the Suzuki-Miyaura reaction is the oxidative addition of the aryl halide to the palladium, forming a Pd (II) intermediate. Then the Pd (II) reacts with base and boronic complex sequentially. Finally, the reductive elimination of Pd (II) gives the product. Thus, we used 43 descriptors related to the reaction mechanism as model input (highlighted atoms and bonds in Figure ). First, we analyzed the yield data for the three datasets and surprisingly found that they have pretty different distributions (Figure ). The HTE dataset contains more low-yield data while the USPTO dataset follows an approximately normal distribution and the Reaxys dataset is highly biased toward high yield, probably only high yield reactions were reported in journal articles where the Reaxys dataset was collected from. Data quality determines the model performance. The difference in distribution implies that when real-world experimental dataset is used, data collection bias needs to be considered. Predicting reaction yield based on real-world data is much more complicated than HTE data, for there are many more factors beyond the reaction components in every reaction process. We used 43 descriptors to train the model and found that XGB outperformed other models on the HTE dataset. Further descriptor analysis (Figure ) showed that the electronic properties of the halide play the most important roles, which is in accordance with the reaction mechanism. It is noteworthy that except for the descriptors about the reactants, the dipole moments of the solvents also remarkably affect the reaction yield. Reaxtica achieved better performance (R 2 = 0.87) than the DL-based method rxnfp or fingerprint-based DRFP using the HTE dataset. We further divided the USPTO and Reaxys dataset into 8:1:1 for training, validation and test, respectively. After a similar process of descriptor analysis, 37 (USPTO) and 9 (Reaxys) descriptors are used in the final model, respectively and Reaxtica performed comparatively (R 2 = 0.214, 0.234 for USPTO and Reaxys dataset, respectively) better than rxnfp and DRFP 27 (Table ). To evaluate the performance of Reaxtica in situation of limited real-world reaction data, the USPTO dataset was trained using 10%, 30%, 50%, 70% and 90% of full data and evaluated using remaining data. The results showed that Reaxtica still outperformed other methods (Table ).