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Research Article | Open Access

Automating selective area electron diffraction phase identification using machine learning

M. Mikaa, N. Tomczakb, C. Finneya, J. Carterb, A. Aitkaliyevaa( )
Nuclear Engineering Program in Department of Materials Science, University of Florida, 100 Rhines Hall, Gainesville, 32611, FL, USA
Department of Materials Science & Engineering, Case Western Reserve University, 2111 Martin Luther King Jr Dr, Cleveland, 44106, OH, USA

Peer review under responsibility of The Chinese Ceramic Society.

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Graphical Abstract

Abstract

Selective area electron diffraction (SAED) patterns can provide valuable insight into the structure of a material. However, the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow. In this work, we utilize the recent advances in computer vision and machine learning (ML) to automate the indexing of SAED patterns. The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys. The most successful approach trained a neural network (NN) to make a classification of the phase and zone axis, and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification. The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.

Keywords

Selective area electron diffractionMachine learningPhase identificationMetallic fuelsPu alloys

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Journal of Materiomics
Volume 10 Issue 4,
July 2024
Pages 896-905
DOI: 10.1016/j.jmat.2023.12.010
Cite this article:
Mika M, Tomczak N, Finney C, et al. Automating selective area electron diffraction phase identification using machine learning. Journal of Materiomics, 2024, 10(4): 896-905. https://doi.org/10.1016/j.jmat.2023.12.010

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Received: 04 October 2023
Revised: 19 December 2023
Accepted: 20 December 2023
Published: 01 February 2024
© 2024 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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