Machine Learning-based Study of Mechanical Properties of Dynamically Crosslinked Polymer Networks

In recent years, polymer composites synthesized from dynamically crosslinked networks have demonstrated improved and innovative properties including shape memory, adhesive, self-healing and malleability. Such polymer networks consist of multiple components that can interact with each other in complex ways. The polymer backbone and the crosslinking agents may be designed in a variety of architectures with the potential to deliver a wide range of physical properties. Understanding the effect of polymer network architecture on the resulting properties of the material is an important and challenging task. Computational models and Machine Learning techniques can provide a useful platform to investigate structure-property relations of crosslinked polymer networks. 

In this study, we utilize Molecular Dynamics (MD) simulations to investigate the relationship between polymer network configuration and the resulting mechanical properties of crosslinked polymer composites. MD simulations are employed to generate stress-strain curves for a variety of crosslinker and backbone polymer configurations. The results of the MD simulations are gathered as the reference data set to be utilized within a Machine Learning (ML) framework. We establish “3D images” of the polymer network configurations obtained from MD simulations and build Convolutional Neural Networks (CNNs) in order to investigate the relationship between the architecture of the network and the mechanical behavior of the material. We discuss the efficiency and accuracy of the CNN in evaluating the mechanical properties of each system and study the impact of the configurational details of each network under set initial conditions. The results of this work provide new insight into the complex architecture of crosslinked polymer networks and help identify material structures that can deliver desired mechanical properties.