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Updated: Nov 3, 2025

Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
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An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction.

Kevin Kaufmann1, Kenneth S Vecchio1

  • 1Department of NanoEngineering, UC San Diego, La Jolla, CA92093, USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

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Deep neural networks for analyzing electron diffraction patterns are robust. Varying experimental conditions like detector tilt or voltage had minimal impact on classification accuracy, ensuring reliable AI in materials science.

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Artificial intelligence (AI) methods, particularly deep neural networks (DNNs), are increasingly used for materials science challenges.
  • Analyzing electron diffraction patterns with DNNs is a key application, but performance under varied experimental conditions is crucial for deployment.

Purpose of the Study:

  • To investigate the impact of varying key experimental parameters on the performance of DNNs used for electron diffraction pattern analysis.
  • To identify which parameters most significantly affect classification accuracy and guide future data collection strategies.

Main Methods:

  • Electron backscatter diffraction (EBSD) patterns were collected while individually varying five parameters: frame averaging, detector tilt, sample-to-detector distance, accelerating voltage, and pattern resolution.
Keywords:
EBSDconvolutional neural networkelectron diffractionmachine learningspace groups

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  • A DNN, pre-trained on EBSD patterns from a fixed condition set, was used to classify patterns generated under these varied conditions.

    • Main Results:

    • The DNN model demonstrated significant resilience to individual variations in most tested experimental parameters.
    • Performance degradation was minimal across a range of altered diffraction conditions, indicating robustness.

    Conclusions:

    • The study suggests that DNNs for electron diffraction analysis are robust to common experimental variations.
    • This resilience builds confidence in deploying AI models across diverse operating conditions in materials science research.