Interpretable machine learning-assisted strategy for predicting the mechanical properties of hydroxyl-terminated polyether binders

Hydroxy-terminated polyether (HTPE) binders are attractive in the weapons materials and equipment industry for their insensitive properties and flexibility. We propose an interpretable machine learning-assisted modeling strategy to predict the mechanical properties of HTPE binders for the first time using machine learning methods. In this strategy, the effects of formulation composition, multiscale characterization, preparation conditions, and mechanical experimental conditions are evaluated on the mechanical properties of HTPE binders. As part of the study, three different techniques were used to predict material properties: bag-based methods (Extra Random Tree, Random Forest), boosting-based methods (XGBoost, CatBoost, and Gradient Boosted Regression), and Artificial Neural Networks (MLPs), all of which were highly accurate in predicting material properties. Based on this, SHAP analysis is used to explain how these influencing factors influence the material properties. An efficient method for examining HTPE binders formulations is provided by this strategy.