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Realizing smart scanning transmission electron microscopy using high performance computing.

Utkarsh Pratiush1, Austin Houston1, Sergei V Kalinin1,2

  • 1Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.

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Summary
This summary is machine-generated.

This study integrates Scanning Transmission Electron Microscopy (STEM) with Electron Energy Loss Spectroscopy (EELS) and machine learning (ML) on High-Performance Computing (HPC) systems. This enhances material characterization efficiency and scope for global STEM users.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Science

Background:

  • Scanning Transmission Electron Microscopy (STEM) coupled with Electron Energy Loss Spectroscopy (EELS) provides rich data for material characterization.
  • Modern electron microscopes generate data at rates exceeding human perception.
  • Machine learning (ML) offers potential to enhance STEM-EELS capabilities through active learning.

Purpose of the Study:

  • To integrate ML algorithms into the STEM-EELS framework for enhanced material characterization.
  • To enable seamless integration of STEM with High-Performance Computing (HPC) systems.
  • To develop and demonstrate workflows for advanced material analysis.

Main Methods:

  • Developed Python server software acting as a wrapper for DigitalMicrograph hardware modules.
  • Facilitated remote computer interactions for STEM-EELS data acquisition and analysis.
  • Implemented sophisticated workflows including object finding and deep kernel learning.

Main Results:

  • Demonstrated seamless integration of STEM-EELS with ML and HPC systems.
  • Showcased enhanced efficiency and expanded scope in material characterization.
  • Enabled advanced analysis techniques for global STEM users with Gatan image filters.

Conclusions:

  • The fusion of STEM-EELS, ML, and HPC significantly advances material characterization capabilities.
  • The developed software and workflows are applicable to a wide range of STEM instruments.
  • Open-source code availability on GitHub promotes broader adoption and further development.