The XGBoost algorithm was found to provide better performance over other algorithms (such as DecisionTree, SVR, MLR, etc). The feature importance analysis showed that the NPA charge at the C-2 position of olefines has the greatest effect on reaction yield (Fig. , Supplementary Table ). The performance of ML models were then evaluated with 33 out-of-sample data (experimentally validated, See Supplementary Table for details), revealing that prediction accuracy was 70%. Furthermore, 3 samples (≈10%) were randomly selected from the 28 'unseen' data to give feedback to the trained models in the secondround learning (Supplementary Table ), demonstrating that the ML model (ML-IV) maintained stability.