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Hansen et al. Microstructures 2023;3:2023029 Microstructures
DOI: 10.20517/microstructures.2023.17

Research Article Open Access

Crystallographic variant mapping using precession

electron diffraction data

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Marcus H. Hansen , Ainiu L. Wang , Jiaqi Dong , Yuwei Zhang , Tejas Umale , Sarbajit Banerjee , Patrick
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Shamberger , Matt Pharr , Ibrahim Karaman , Kelvin Y. Xie 1
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Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA.
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Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA.
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Department of Chemistry, Texas A&M University, College Station, TX 77843, USA.
Correspondence to: Prof. Kelvin Y. Xie, Department of Materials Science and Engineering, Texas A&M University, College
Station, TX, 77843, USA. E-mail: kelvin_xie@tamu.edu
How to cite this article: Hansen MH, Wang AL, Dong J, Zhang Y, Umale T, Banerjee S, Shamberger P, Pharr M, Karaman I, Xie
KY. Crystallographic variant mapping using precession electron diffraction data. Microstructures 2023;3:2023029.
https://dx.doi.org/10.20517/microstructures.2023.17
Received: 15 Apr 2023 First Decision: 4 May 2023 Revised: 7 Jun 2023 Accepted: 19 Jun 2023 Published: 5 Jul 2023
Academic Editor: Xiaozhou Liao Copy Editor: Fangyuan Liu Production Editor: Fangyuan Liu
Abstract
In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by
analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO thin film on
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sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an
algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches,
Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern
similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine
algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity
quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods
mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling
fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of
each approach are also discussed. We expect these methods to benefit researchers who work on martensitic
materials, in which the variant information is critical to understand their properties and functionalities.

Keywords: Crystallographic variant mapping, precession electron diffraction (PED), image similarity quantification,
k-means

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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