Using gas uptake data, we fit linear regression and random forest models using the scikitlearn 1.3.0 Python package . For all models, 20% of the data were randomly selected as a setaside test set. From the remaining 80% training set, an exhaustive grid search was used for 5-fold cross-validation (CV) hyperparameter optimization (Supporting Information Table ). The error metric for regression tasks was the mean absolute error (MAE).