First, we rapidly tested 30 CML models with Lazy Predict package to predict both E f and E g in the MP dataset. The performance of the top 15 CML models was summarized in Tables and. Among them, Random Forest (RF) , Gradient Boosting (GB) , Light Gradient Boosting Machine (LGBM) , and eXtreme Gradient Boosting (XGB) emerged as the most superior performers. We further optimized these models by fine-tuning their hyperparameters, with the detailed hyperparameter settings provided in Tables . tasks. Subsequently, a pooling layer aggregates the encoded information to produce an overall feature vector using various pooling functions, such as summation, averaging, or maximization.