Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers

In development, precise measurement and control of Tg is essential for researchers to thoroughly understand and master how variations in formulation and production processes impact product performance. Given the complexity of industrial production, most manufacturers repeatedly produce fluorine materials and test their Tg until it satisfies the application environment’s demands. Consequently, this study utilized an FKM dataset from actual synthesis trials, subjected to data classification, alignment, and feature filtering, to develop a predictive model based on Elastic Net Regression, enhancing the accuracy of Tg predictions for FKM. We evaluated the performance of several regression machine learning algorithms, including Random Forest, Support Vector Machines, Decision Trees, Extreme Gradient Boosting, and Ensemble Learning. The findings show that Random Forest and Elastic Net models exceeded other models by approximately 5% to 10 % in the coefficient of determination, indicating superior predictive capabilities. The Elastic Net Regression model excelled particularly in terms of evaluation coefficients and error rates. To boost the model’s interpretability, this research developed and implemented a visualization technology scheme for the excellently performing models, integrating SHAP value analysis to vividly depict the feature space, prediction processes, and experimental outcomes, thereby aiding the exploration of Tg influencing factors. This research not only offers an innovative machine learning framework for Tg prediction in FKM but also enhances the model’s transparency and credibility using visualization technology, holding significant theoretical and practical implications for materials science research.