Machine-learning-assisted reaction space exploration. Recently, machine learning (ML) methodologies were demonstrated to be useful in the prediction of synthetic performance . Here, several ML models were adopted to predict the reaction yield using 7~10 features (e.g., calculated NPA charges, molecular volume , and bond dissociation energies, etc. see Supplementary Tables and). In the first round, eight features were chosen to build the ML model (ML-IV) after feature selection (Fig. , and Supplementary Tables 6~9), 43 experimental data were collected to train the ML model (Supplementary Fig. , Tables ).