Patterns (Nov 2020)
Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity
Abstract
Summary: The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties. It was determined that a unified parameter with topological and spatial information universally describes some parameters related to the stresses. This approach enables us to uncover the role of individual crosslinking points for the stresses, even in complicated structures. Based on the data science, we anticipate that the structure-property relationships of heterogeneous materials can be interpretatively represented using this type of “white box” approach. The Bigger Picture: Complex network science has contributed to extracting essential parameters from network structure and has been applied in social, geographical, computer, and biological sciences. On the other hand, in materials science, some materials possess a network structure that determines their properties. Because both connectivity and spatial distance are significant factors in materials, utilizing a combined descriptor to explain their properties could be important. In this study, we demonstrate that the descriptor with both connectivity and spatial distance prior to elongations universally represented some parameters related to mechanical properties during elongation, which enabled us to interpret the role of each node. Recently, there have been significant attempts to develop new materials by methods in data science such as materials informatics. Thus, our approaches could contribute in the future to the development of materials with network structures in an interpretable manner.
