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  1. Home
  3. Interpretable Liquid Crystal Phase Classification Via Two-by-two Ordinal Patterns.
  1. Home
  3. Interpretable Liquid Crystal Phase Classification Via Two-by-two Ordinal Patterns.

Related Experiment Video

Orientational Transition in a Liquid Crystal Triggered by the Thermodynamic Growth of Interfacial Wetting Sheets
06:26

Orientational Transition in a Liquid Crystal Triggered by the Thermodynamic Growth of Interfacial Wetting Sheets

Published on: May 15, 2017

Interpretable liquid crystal phase classification via two-by-two ordinal patterns.

Leonardo G J M Voltarelli1, Natalia Osiecka-Drewniak2, Marcin Piwowarczyk2

  • 1Universidade Estadual de Maringá, Departamento de Física, Maringá, PR 87020-900, Brazil.

Physical Review. E
|May 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed an interpretable method using ordinal patterns to analyze liquid crystal textures, achieving high accuracy in identifying mesophases. This approach offers a scalable and understandable alternative to complex deep learning models for materials science image analysis.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Data Science

Background:

  • Liquid crystal textures contain valuable structural data, but their interpretation is complex.
  • Similar visual patterns can originate from different underlying structures, hindering accurate mesophase identification.

Purpose of the Study:

  • To develop a simple, interpretable method for mapping liquid crystal textures to their corresponding mesophase identity.
  • To create a scalable tool for analyzing complex patterned systems in materials science.

Main Methods:

  • A 75-dimensional frequency vector of two-by-two ordinal patterns was generated from liquid crystal textures.
  • Ordinal patterns were grouped into 11 symmetry-based types for mesophase characterization.
  • A simple machine learning classifier was employed with the ordinal pattern representation.

Main Results:

  • Near-perfect recognition of seven distinct mesophases was achieved, including differentiating between smectic A and smectic B.
  • The method accurately distinguished between phase identity and material origin, generalizing to unseen compounds.
  • Model explanations and network visualizations provided insights into texture determinants.

Conclusions:

  • Two-by-two ordinal patterns offer an interpretable and scalable approach for liquid crystal image analysis.
  • This method provides a physically meaningful summary of texture characteristics.
  • The approach has potential applications beyond liquid crystals to other patterned materials.