Computer Generation of Disordered Networks with Targeted Structural Properties

Abstract

Disordered spatial networks describe structures and interactions across multiple length scales. The scattering and interference of waves within these networks result in structural phase transitions, localization, diffusion, and band gaps. Studying these phenomena requires efficient numerical methods for generating disordered networks with specific structural properties. The Wooten-Weaire-Winer algorithm is an established method that introduces disorder into an initial network through a series of bond switch moves. However, the strain energies that govern this evolution are conventionally limited to three-dimensional networks with coordination numbers of no more than four. We here introduce a maximum bond repulsion to produce networks with an arbitrary coordination number. We control the degree and type of disorder by adjusting the bond-bending force constant in the strain energy and the temperature profile. The effects of these variables are quantified through a list of order metrics that capture both direct and reciprocal space. A feedforward neural network predicts the structural characteristics from the algorithm inputs, enabling efficient targeted network generation. As a case study, we statistically reproduce four disordered biophotonic networks that exhibit structural color. This work presents a versatile method for generating disordered networks with tailored structural properties. It will provide new insights into structure-property relations.