Data-driven electron microscopy: Electron diffraction imaging of materials structural properties

Jian Min Zuo, Renliang Yuan, Yu Tsun Shao, Haw Wen Hsiao, Saran Pidaparthy, Yang Hu, Qun Yang, Jiong Zhang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)I116-I131
JournalMicroscopy (Oxford, England)
StatePublished - Mar 1 2022
Externally publishedYes


  • 4D-STEM
  • electron nanodiffraction
  • fast electron detectors
  • machine learning
  • orientation and strain mapping

ASJC Scopus subject areas

  • General Medicine


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