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Deep Learning Analysis of Crystallization Using Polarized Light Microscopy and U-Net Segmentation.

Natalia Osiecka-Drewniak1, Zbigniew Galewski2, Marcin Piwowarczyk1

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This study combines polarized light microscopy and deep learning to analyze the crystallization of liquid crystals. The AI model accurately identifies crystalline and smectic phases, revealing crystallization kinetics during cooling.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Science

Background:

  • Crystallization behavior is crucial for material properties.
  • Liquid crystals exhibit complex phase transitions.
  • Automated analysis of phase transitions is challenging.

Purpose of the Study:

  • To develop a novel method for analyzing material crystallization.
  • To investigate the crystallization kinetics of liquid-crystalline compound 9BA4.
  • To combine optical microscopy with deep learning for quantitative analysis.

Main Methods:

  • Utilized polarized light microscopy to observe crystallization.
  • Employed a U-Net convolutional neural network for semantic segmentation of textures.
  • Performed nonisothermal cooling at multiple rates.
  • Analyzed crystallization kinetics using sigmoidal fitting and the Ozawa model.

Main Results:

  • Successfully trained a U-Net model to identify crystalline (Cr) and smectic (SmC) phases.
  • Quantified the degree of crystallization over temperature using probability maps.
  • Determined the temperature of maximum crystallization.
  • Demonstrated effective extraction of quantitative insights from texture evolution.

Conclusions:

  • The combined approach of microscopy and deep learning provides effective quantitative insights.
  • This methodology enhances the understanding of complex phase transitions in materials.
  • Automated image analysis accelerates the study of crystallization kinetics.