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A deep learning approach using Siamese neural networks successfully distinguished subtle phase transitions in liquid crystals by analyzing microscopic textures. This method effectively differentiates liquid crystal phases from their glassy states.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Artificial Intelligence

Background:

  • Liquid crystals exhibit complex phase transitions, often involving subtle changes in molecular ordering.
  • Distinguishing between liquid crystal phases and their corresponding glassy states can be challenging due to similar microscopic textures.
  • Polarizing microscopy is a key technique for visualizing these textures and identifying phase behaviors.

Purpose of the Study:

  • To apply a deep learning technique, specifically a Siamese neural network, for analyzing phase transitions in liquid crystals.
  • To investigate the capability of Siamese neural networks in differentiating subtle transitions, such as between liquid crystal phases and their glasses.
  • To detail the implementation and testing of a Siamese neural network using various convolutional neural network architectures.

Main Methods:

  • Utilized a Siamese neural network, a deep learning architecture designed for comparing image pairs.
  • Employed polarizing microscopic textures of liquid crystals, including antiferroelectric smectic CA*, smectic I, and smectic G phases and their glasses.
  • Tested the Siamese neural network implementation with three distinct convolutional neural network models.

Main Results:

  • The Siamese neural network demonstrated a high capability in distinguishing between the textures of specific liquid crystal phases and their glassy counterparts.
  • The network successfully identified subtle phase transitions where traditional structural changes are minimal.
  • Performance was evaluated across different convolutional neural network architectures integrated into the Siamese framework.

Conclusions:

  • Siamese neural networks offer a powerful and effective tool for analyzing subtle phase transitions in liquid crystals based on microscopic texture analysis.
  • This deep learning approach provides a robust method for differentiating between distinct phases and their glassy states, even with visually similar textures.
  • The study highlights the potential of AI in advancing the understanding and characterization of complex materials like liquid crystals.