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Active nematics are dense systems of rodlike particles that consume energy todrive motion at the level of the individual particles. They exist in naturalsystems like biological tissues and artificial materials such as suspensions ofself-propelled colloidal particles or synthetic microswimmers. Active nematicshave attracted significant attention in recent years due to their spectacularnonequilibrium collective spatiotemporal dynamics, which may enableapplications in fields such as robotics, drug delivery, and materials science.The director field, which measures the direction and degree of alignment of thelocal nematic orientation, is a crucial characteristic of active nematic and isessential for studying topological defects. However, determining the directorfield is a significant challenge in many experimental systems. Althoughdirector fields can be derived from images of active nematics using traditionalimaging processing methods, the accuracy of such methods are highly sensitiveto the settings of the algorithms. These settings must be tuned fromimage-to-image due to experimental noise, intrinsic noise of the imagingtechnology, and perturbations caused by changes in experimental conditions.This sensitivity currently limits automatic analysis of active nematics. Toaddress this, we developed a machine learning model for extracting reliabledirector fields from raw experimental images, which enables accurate analysisof topological defects. Application of the algorithm to experimental datademonstrates that the approach is robust and highly generalizable toexperimental settings that are different from those in the training data. Itcould be a promising tool for investigating active nematics and may begeneralized to other active matter systems.

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