Data-Driven Electron Microscopy: Electron Diffraction Imaging of Materials Structural Properties

TL;DR

This paper reviews recent advances in data-driven electron microscopy, focusing on electron diffraction techniques, new algorithms for diffraction pattern analysis, and future prospects involving machine learning for materials characterization.

Contribution

It provides a comprehensive overview of recent progress in data collection, algorithm development, and automation in electron diffraction imaging for materials research.

Findings

Enhanced data collection methods for electron diffraction patterns.

Development of new algorithms for diffraction pattern analysis.

Discussion of future opportunities with machine learning and smart sampling.

Abstract

Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. Here, we review recent progress made in data collection, new algorithms, and automated electron DP analysis. These progresses will be highlighted using application examples in materials research. Future opportunities based on smart sampling and machine learning are also discussed.