The structure of polymer networks, defined by chain lengths and connectivity patterns, fundamentally influences their bulk properties. Despite decades of research, many open questions remain regarding structure-property relationships, particularly in heterogeneous networks. While existing polymer network models connect chain properties to emergent network behavior, they are often limited to monodisperse networks with regular connectivities, making the extension to heterogeneous systems an active area of research. In this work, we introduce a novel modeling framework that shifts the focus from individual polymer chains to cross-links and their connected chains as the fundamental unit of analysis. This perspective enables direct incorporation of structural heterogeneity by averaging over cross-links with different chain lengths and coordination numbers. We establish important connections between polymerization theory and statistical descriptors of network structure, providing key components of a theoretical foundation for predicting properties from the synthesis parameters. Our results demonstrate that increased variance in monomer numbers generally leads to network softening, while in bimodal networks, the onset of strain stiffening is controlled by shorter chains and the stiffening response is modulated by the ratio of short to long chains. By deriving closed-form approximations valid in both the limits of (1) small deformation and (2) finite deformation but with small polydispersity, we offer an efficient computational approach to modeling complex networks. This framework represents an advance toward the rational modeling and design of heterogeneous polymer networks with structures tailored for specific properties.
Mulderrig et al. (Thu,) studied this question.