Importantly, the hyperparameter search, which is uniform across all four CML models, constitutes the majority of the time consumed. As for GNN based models, the total computational time includes a training process of 300 epochs followed by a testing step. Default hyperparameters were employed without additional optimization. Figure presents the time consumption per data amount for eight ML models in predicting E f values across three datasets. Among CML models, LGBM and XGB models demonstrate superior computational efficiency, with a computational duration of less than 0.09 s per data point, compared to RF and GB models (more than 0.16 s per data point) across any given dataset. It is worth noting that LGBM and XGB models also exhibit superior prediction accuracy, evidenced by higher R 2 values compared to RF and GB models (see in Table ). The concurrent attainment of high accuracy and computational efficiency positions LGBM and XGB models as the top choices among various CML models, which were also evidenced by previous studies. In fact, LGBM and XGB leverage techniques like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) , which significantly accelerate the training process and reduce memory consumption.