For predicting MCA and MAA values, we train a light gradient boosting machine (LightGBM) regression model for each property. The LightGBM model is chosen based on previous benchmark studies of several ML models in combination with the atom-based feature vectors. The hyperparameters for LightGBM models are obtained using a tree-structured Parzen estimator (TPE) as implemented in Optuna version 2.5.0. 37 This Bayesian optimization method efficiently explores a large hyperparameter space to identify the configurations that minimize the root mean square error (RMSE). For each set of hyperparameters as well as the final model training, we conduct a 5-fold cross-validation to ensure a robust model performance. The cross-validation involves a stratified split of the binned training set, which helps maintain the distribution of target values across the folds, and we apply random shuffling to avoid any bias in the data. This ensures that the model is evaluated on diverse subsets of the data, providing a more comprehensive assessment of its predictive performance. After the final model training, we only retain the model with the lowest RMSE from the 5-fold cross-validation.