Can we gain insight about the ductile behavior of materials by using polymer informatics?

Abstract

Predicting the ductile behavior of thermoplastic materials is a significant challenge for both industry and research. In this study, we present a predictive model that classifies polymers based on their ductility degree, which is a relationship created in the present work to approach this problem. It comes from relating two critical and measurable properties from the tensile test. This target was discretized into three classes, more ductile, intermediate, and less ductile. The feature selection process employed for finding the most relevant molecular descriptors for the predictive model used two approaches: a classical one and an expert-guided one. A new metric, called relaxed %CC, was presented to prioritize the models that reduce the misclassification between the extremes of the ductile scale, which is considered more important than confusion with the intermediate class. Our final model was able to successfully classify polymers, achieving a precision rate of 0.91, an %CC of 89.47 % (traditional accuracy), and a relaxed %CC of 100 % (no extremes confusion). This approach has the potential to help both the industry and R&D by selecting polymers with suitable ductile properties for specific applications during the design stage before their synthesis.