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Spherical-angular dark field imaging and sensitive microstructural phase clustering with unsupervised machine

T P McAuliffe1, D Dye1, T B Britton1

  • 1Department of Materials, Imperial College London, Prince Consort Road, London, UK.

Ultramicroscopy
|October 14, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning effectively distinguishes subtle differences in electron backscatter patterns, enabling detailed crystallographic analysis. This method enhances mapping of crystal structures in various materials using scanning electron microscopy.

Keywords:
EBSDMachine LearningMicrostructureSuperalloyVirtual imaging

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Area of Science:

  • Materials Science
  • Crystallography
  • Data Science

Background:

  • Electron backscatter diffraction (EBSD) is crucial for analyzing nano- to micro-scale crystal structures.
  • Distinguishing ordered superlattices from alloy matrices in EBSD is challenging due to subtle pattern differences caused by inelastic scattering.

Purpose of the Study:

  • To apply unsupervised machine learning for enhanced feature extraction and classification of superlattice and matrix structures in EBSD.
  • To develop a pipeline for detailed crystallographic mapping in scanning electron microscopy.

Main Methods:

  • Utilized unsupervised machine learning algorithms: Principal Component Analysis (PCA), Non-negative Matrix Factorisation (NMF), and an Autoencoder neural network.
  • Developed a method to remap cluster average patterns onto the diffraction sphere for comparison with simulations.

Main Results:

  • Unsupervised machine learning successfully extracted fine features and classified superlattice/matrix structures.
  • The pipeline enabled comparison of Kikuchi band profiles to dynamical simulations, confirmation of superlattice stoichiometry, and virtual imaging.

Conclusions:

  • Unsupervised machine learning provides a robust approach for analyzing subtle EBSD pattern variations.
  • This technique significantly advances the capability for high-resolution crystallographic mapping of diverse materials.