Structural Analysis of Nanoscale Network Materials Using Graph Theory

Many materials with remarkable properties are structured as percolating nanoscale networks (PNNs). The design of this rapidly expanding family of composites and nanoporous materials requires a unifying approach for their structural description. However, their complex aperiodic architectures are difficult to describe using traditional methods that are tailored for crystals. Another problem is the lack of computational tools that enable one to capture and enumerate the patterns of stochastically branching fibrils that are typical for these composites. Here, we describe a computational package, StructuralGT, to automatically produce a graph theoretical (GT) description of PNNs from various micrographs that addresses both challenges. Using nanoscale networks formed by aramid nanofibers as examples, we demonstrate rapid structural analysis of PNNs with 13 GT parameters. Unlike qualitative assessments of physical features employed previously, StructuralGT allows researchers to quantitatively describe the complex structural attributes of percolating networks enumerating the network's morphology, connectivity, and transfer patterns. The accurate conversion and analysis of micrographs was obtained for various levels of noise, contrast, focus, and magnification, while a graphical user interface provides accessibility. In perspective, the calculated GT parameters can be correlated to specific material properties of PNNs (e. g., ion transport, conductivity, stiffness) and utilized by machine learning tools for effectual materials design.

Automated summary

Materials with remarkable properties are structured as percolating nanoscale networks (PNNs), but their complex structures are difficult to describe using traditional methods and lack computational tools to capture patterns. The computational package StructuralGT generates GT descriptions of PNNs from micrographs, allowing rapid analysis of their structures. The GT parameters calculated can be correlated to specific material properties of PNNs for effective materials design.