Our findings suggest that E min has the potential to augment existing scores to create a combined predictor whose accuracy exceeds either score alone. To further explore this idea, we combine each of the SA, SC, RA, SYBA scores individually with E min by using a logistic regression model. Logistic regression models are simple statistical models that find a single optimal feature boundary expressed as a weighted sum of multiple features. The model performs the best with features that are monotonic, which is true for all synthesizability scores. With our logistic regression models, we Table : Accuracy improvements when using E min in logistic regression models. Accuracies for synthesizability scores by themselves "(Score)" and with E min in logistic regression models "(Score+E min )" when performing 10-fold cross validation. Accuracies are averaged across all folds with standard deviations reported for the third decimal place in brackets (i.e. [X] = 0.00X). Accuracies are reported as the 1) area under the ROC curve (AUC), where higher values mean greater accuracy, and 2) false discovery rate (FDR), which is the proportion of synthesizable predictions that are incorrect given a decision threshold of 0.50 (lower values mean greater accuracy). Accuracies for the logistic regression models are always better than those for standalone scores, demonstrating that E min can effectively augment other scores.