A machine learning approach to robustly determine director fields and analyze defects in active nematics

Area of Science:

• Physics
• Materials Science
• Biophysics

Background:

• Active nematics are systems of self-propelled rodlike particles exhibiting complex nonequilibrium dynamics.
• These systems are found in biological tissues and artificial materials, with potential applications in robotics and drug delivery.
• The director field, indicating local particle alignment, is crucial for studying topological defects but challenging to determine experimentally.

Purpose of the Study:

• To develop a robust method for accurate director field extraction from active nematic images.
• To overcome limitations of traditional image processing methods sensitive to experimental noise and conditions.
• To enable reliable automated analysis of topological defects in active nematics.

Main Methods:

• A machine learning model was developed to process raw experimental images of active nematics.
• The model learns to extract reliable director fields, essential for defect analysis.
• The algorithm was trained and validated on experimental data.

Main Results:

• The machine learning model reliably extracts director fields from experimental images.
• The developed approach enables accurate analysis of topological defects.
• The method demonstrates robustness and generalizability across different experimental settings.

Conclusions:

• The machine learning model provides a promising tool for investigating active nematics.
• This approach overcomes the sensitivity limitations of traditional image processing techniques.
• The methodology may be adaptable to other active matter systems.