Topological defect coarsening in quenched smectic-C films analyzed using artificial neural networks

TL;DR

This study uses neural networks to analyze the formation and evolution of topological defects in quenched smectic-C liquid crystal films, revealing defect dynamics consistent with theoretical models.

Contribution

It introduces a novel application of convolutional neural networks for defect detection and sign classification in liquid crystal textures.

Findings

Defect annihilation dynamics match 2D XY model predictions

Neural networks enable automated defect detection and sign determination

Temporal evolution of defect density follows theoretical scaling laws

Abstract

Mechanically quenching a thin film of smectic-C liquid crystal results in the formation of a dense array of thousands of topological defects in the director field. The subsequent rapid coarsening of the film texture by the mutual annihilation of defects of opposite sign has been captured using high-speed, polarized light video microscopy. The temporal evolution of the texture has been characterized using an object-detection convolutional neural network to determine the defect locations, and a binary classification network customized to evaluate the brush orientation dynamics around the defects in order to determine their topological signs. At early times following the quench, inherent limits on the spatial resolution result in undercounting of the defects and deviations from expected behavior. At intermediate to late times, the observed annihilation dynamics scale in agreement with theoretical predictions and simulations of the $2$D XY model.