Detection and tracking of defects in the gyroid mesophase

TL;DR

This paper introduces two automated methods for detecting and tracking defects in gyroid mesophases, improving analysis efficiency over manual inspection in large datasets.

Contribution

It presents and compares a pattern recognition algorithm and a structural property-based approach for defect detection in gyroid mesophases.

Findings

The pattern recognition method effectively identifies defects.

The structural property-based method leverages gyroid symmetry.

Both methods outperform manual analysis in speed.

Abstract

Certain systems, such as amphiphile solutions or diblock copolymer melts, may assemble into structures called ``mesophases'', with properties intermediate between those of a solid and a liquid. These mesophases can be of very regular structure, but may contain defects and grain boundaries. Different visualization techniques such as volume rendering or isosurfacing of fluid density distributions allow the human eye to detect and track defects in liquid crystals because humans are easily capable of finding imperfections in repetitive spatial structures. However, manual data analysis becomes too time consuming and algorithmic approaches are needed when there are large amounts of data. We present and compare two different approaches we have developed to study defects in gyroid mesophases of amphiphilic ternary fluids. While the first method is based on a pattern recognition algorithm, the second uses the particular structural properties of gyroid mesophases to detect defects.