  Epoxy polymers are used in wide range of applications. The properties and
performance of epoxy polymers depend upon various factors like the type of
constituents and their proportions used and other process parameters. The
conventional way of developing epoxy polymers is usually labor-intensive and
may not be fully efficient, which has resulted in epoxy polymers having a
limited performance range due to the use of predetermined blend combinations,
compositions and development parameters. Hence, in order to experiment with
more design parameters, robust and easy computational techniques need to be
established. To this end, we developed and analyzed in this study a new machine
learning (ML) based approach to predict the mechanical properties of epoxy
polymers based on their basic structural features. The results from molecular
dynamics (MD) 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.
