Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques

The conventional way of developing epoxy polymers may not be fully efficient as it is constrained by fixed compositions that limit performance. To overcome this, we introduce a machine learning (ML)-based approach that accurately predicts mechanical properties from its basic structural features, enabling broader design exploration. The results from molecular dynamics simulations have been used to derive the ML model. The salient feature of our work is that for the development of epoxy polymers based on EPON-862, several new hardeners were explored in addition to the conventionally used ones. The influence of additional parameters like the proportion of curing agent used and the extent of curing on the mechanical properties of epoxy polymers were also investigated. This method can be further extended by providing the epoxy polymer with the desired properties through knowledge of the structural characteristics of its constituents. The findings of our study can thus lead toward development of efficient design methodologies for epoxy polymeric systems.