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Machine-learning-based atom probe crystallographic analysis.

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Summary
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This study introduces a deep-learning method to automatically analyze crystallographic patterns from atom probe tomography detector data. This advances nanoscale materials analysis by making crystallographic information more accessible and reliable.

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Area of Science:

  • Materials Science
  • Data Science
  • Nanotechnology

Background:

  • Atom probe tomography (APT) excels at nanoscale compositional analysis.
  • Crystallographic information is present in APT detector hit patterns but is largely unexploited due to manual analysis.
  • Automating the interpretation of these patterns is crucial for unlocking their full potential.

Purpose of the Study:

  • To develop an automated approach for interpreting crystallographic patterns in atom probe tomography data.
  • To leverage deep learning and image analysis for efficient and reliable extraction of structural and orientational information.
  • To overcome the limitations of manual analysis and make crystallographic APT accessible to a wider user base.

Main Methods:

  • Combined image analysis for feature selection and deep learning for pattern interpretation.
  • Unsupervised machine learning to train a deep neural network using theoretical crystallographic relationships.
  • Application to detector hit maps from pure aluminum samples under various experimental conditions.

Main Results:

  • Demonstrated successful automated interpretation of crystallographic patterns directly from detector hit maps.
  • The approach proved robust across different temperatures, pulsing modes, and pulse fractions.
  • Benchmarked against Hough-transform methods, showing improved efficiency and reliability.

Conclusions:

  • The proposed deep-learning approach significantly enhances the efficiency, sensitivity, robustness, and reliability of crystallographic atom probe tomography analysis.
  • Automated interpretation of detector hit patterns provides direct access to combined crystallographic and compositional nanoscale information.
  • Deep learning holds great potential for democratizing advanced materials characterization techniques.