A Machine Learning Approach to Robustly Determine Director Fields and Analyze Defects in Active Nematics

TL;DR

This paper introduces a machine learning method to accurately and robustly determine director fields in active nematics from experimental images, overcoming limitations of traditional image processing techniques.

Contribution

A novel machine learning approach for extracting director fields from images of active nematics, enhancing robustness and generalizability over traditional methods.

Findings

The model accurately identifies director fields in noisy experimental images.

The approach is robust across different experimental conditions.

It enables precise analysis of topological defects in active nematics.

Abstract

Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They exist in natural systems like biological tissues and artificial materials such as suspensions of self-propelled colloidal particles or synthetic microswimmers. Active nematics have attracted significant attention in recent years due to their spectacular nonequilibrium collective spatiotemporal dynamics, which may enable applications in fields such as robotics, drug delivery, and materials science. The director field, which measures the direction and degree of alignment of the local nematic orientation, is a crucial characteristic of active nematic and is essential for studying topological defects. However, determining the director field is a significant challenge in many experimental systems. Although director fields can be derived from images of active nematics using traditional imaging processing methods, the accuracy of such methods are highly sensitive to the settings of the algorithms. These settings must be tuned from image-to-image due to experimental noise, intrinsic noise of the imaging technology, and perturbations caused by changes in experimental conditions. This sensitivity currently limits automatic analysis of active nematics. To address this, we developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects. Application of the algorithm to experimental data demonstrates that the approach is robust and highly generalizable to experimental settings that are different from those in the training data. It could be a promising tool for investigating active nematics and may be generalized to other active matter systems.