StructuralGT v2.0: Graph theoretic characterization and modeling of complex networked materials

Structural analysis of networked materials is a fundamental component in understanding their mechanical, electrical and optical properties. We introduce StructuralGT 2.0 – a graph theoretic software package for analyzing complex networked materials – and demonstrate its applicability to self-assembled nanostructures. In this release, users may now analyze graphs from 3- and 2-dimensional electron microscopy datasets as well as simulated datasets, and integrate their own fast C routines that can operate on Python graphs. This allows for integration with other data-analytic tools from the Python ecosystem, while enabling a previously impossible scale of networked material characterization. To showcase its new capabilities, we demonstrate how we use StructuralGT 2.0 to accurately predict the conductivities, charge carrying capacities, and anisotropy of multilayer silver nanowire films. We also show the graph theoretic characterization of networks of percolating aramid nanofibers. We expect that this tool, which offers a quantitative basis for graph theoretical analysis, will be critical in discovering and designing structure-property relationships for complex networked materials.