We employed six conventional ML algorithms: Hist Gradient Boosting regression (HGBR), Gradient Boosting Regression (GBR), LightGBM regression (LGBM), Extreme Gradient Boosting regression (XGBoost), AdaBoost regression (AdaBoost), random forest (RF). These models are widely used in materials science and chemistry to uncover structure-property relationships. ML model training was performed with Scikit-learn library. The details of the training process can be found in Note S2. Performance metrics, including coefficient of determination (R 2 ), Pearson correlation coefficient (r), Root mean square error (RMSE), and mean absolute error (MAE), were defined in Note S3.