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Identifying local structural states in atomic imaging by computer vision.

Nouamane Laanait1, Maxim Ziatdinov1, Qian He2

  • 1Institute for Functional Imaging of Materials, Oak Ridge, 37831 TN USA ; Center for Nanophase Materials Sciences, Oak Ridge, 37831 TN USA.

Advanced Structural and Chemical Imaging
|November 22, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computer vision approach to identify and classify local structural states in materials, independent of symmetry and periodicity assumptions. The method accurately analyzes atomic imaging data, advancing materials science investigations.

Keywords:
Computer visionScanning transmission electron microscopyScanning tunneling microscopyUnsupervised machine learning

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Area of Science:

  • Materials Science
  • Computer Vision
  • Crystallography

Background:

  • Atomically resolved imaging reveals local structural states deviating from periodicity and symmetry.
  • Extracting these states with minimal assumptions is crucial for materials investigation.

Purpose of the Study:

  • To develop a computer vision-based approach for identifying and classifying local structural states in materials.
  • To create a method independent of traditional symmetry and periodicity concepts.

Main Methods:

  • Utilized computer vision techniques like feature detection and scale invariance.
  • Defined structural states using both local and nonlocal information from atomic images.
  • Applied to simulated scanning transmission electron microscopy (STEM) images.

Main Results:

  • Demonstrated robustness against noise, limited resolution, and weak contrast in simulated data.
  • Successfully applied to experimental electron microscopy and scanning tunneling microscopy data.
  • Enabled unsupervised detection and classification of local structural states at material interfaces and surfaces.

Conclusions:

  • The computer vision approach offers a powerful, assumption-free method for analyzing local structural states in materials.
  • This technique enhances the investigation of complex materials and interfaces using atomic imaging data.