In this study, we also assess the ability to improve synthesizability predictions using machine learning (ML) models informed by E min and other features. Our ML models use a random forest classifier from the Scikit-learn python package, and predict class probabilities between 0 and 1 to represent a molecule's synthesizability (1 = synthesizable, 0 = unsynthesizable). We use 100 trees in our models, which yield a favorable balance between accuracy and computational efficiency. All other hyperparameters are left as their default values. Accuracies are evaluated by performing 10-fold cross validation.