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Automated atomic site determination by four-dimensional scanning transmission electron microscopy data analytics.

Francisco Fernandez-Canizares1, Javier Rodriguez-Vazquez1, Rafael V Ferreira1

  • 1GFMC, Departamento de Física de Materiales, Universidad Complutense de Madrid, Madrid, 28040, Spain; Instituto Pluridisciplinar Universidad Complutense de Madrid, 28040, Spain.

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Summary
This summary is machine-generated.

This study introduces a new automated method for identifying atomic columns in advanced materials using clustering and dimensionality reduction. This technique accurately detects atomic sites across diverse materials, enabling detailed data-driven analysis.

Keywords:
4D-STEMConvergent beam electron diffractionDeep learningMachine learningScanning transmission electron microscopy

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Area of Science:

  • Materials Science
  • Data Science
  • Microscopy

Background:

  • Automated atomic column detection is crucial for advanced scanning transmission electron microscopy (STEM).
  • Identifying atomic columns in diverse materials (2D, semiconductors, oxides) with varying column types presents a significant challenge.
  • Current methods may struggle with the complexity and variety of atomic structures encountered in cutting-edge materials.

Purpose of the Study:

  • To develop and implement an automated pipeline for atomic column detection and identification in advanced materials.
  • To address the challenges posed by diverse material types and column characteristics (heavy/light).
  • To enable data-driven analysis of material properties through accurate atomic site identification.

Main Methods:

  • Utilized clustering algorithms combined with dimensionality reduction techniques.
  • Implemented a three-stage cascaded clustering pipeline.
  • Applied the method to a range of cutting-edge materials, including 2D materials, bulk semiconductors, and complex oxides.

Main Results:

  • Successfully identified all atomic column sites across various test materials.
  • Effectively resolved atomic columns from the background interatomic space.
  • Demonstrated robustness in detecting different types of atomic columns (heavy and light).

Conclusions:

  • The developed three-stage cascaded clustering pipeline enables robust automated atomic column detection.
  • This approach facilitates in-depth, data-driven analysis of material characteristics.
  • The method holds potential for automatic detection of chemical and structural properties in materials.