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Classification for transmission electron microscope images from different amorphous states using persistent homology.

Fumihiko Uesugi1, Masashi Ishii2

  • 1Electron Microscopy Analysis Station, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.

Microscopy (Oxford, England)
|March 14, 2022
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Summary

Persistent homology effectively distinguishes amorphous and liquid states in transmission electron microscopy (TEM) images. This mathematical technique offers reliable discrimination across various focus conditions, overcoming limitations of traditional methods.

Keywords:
GaNTEM image simulationamorphous structurepersistent homology

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Area of Science:

  • Materials Science
  • Computational Science
  • Data Analysis

Background:

  • Discriminating amorphous states in transmission electron microscopy (TEM) is challenging.
  • Traditional methods like radial distribution function are limited by imaging conditions such as focus and sample thickness.

Purpose of the Study:

  • To develop a robust method for discriminating between different amorphous states using TEM images.
  • To apply persistent homology and machine learning for enhanced analysis of amorphous material structures.

Main Methods:

  • Generated structural models of amorphous and liquid states via classical molecular dynamics simulations.
  • Simulated TEM images under various defocus conditions using the multi-slice method.
  • Calculated persistent diagrams and applied logistic regression and support vector classification for discrimination.

Main Results:

  • Achieved over 85% accuracy in discriminating between amorphous and liquid phases.
  • Demonstrated that persistent homology is effective across a wide range of focus conditions.
  • Overcame limitations of radial distribution function in classifying amorphous states.

Conclusions:

  • Persistent homology provides a powerful tool for analyzing and differentiating amorphous states in TEM.
  • This approach offers a reliable alternative to traditional methods, especially under varying imaging parameters.
  • The combination of persistent homology and machine learning enhances the characterization of disordered materials.