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Unsupervised learning of ferroic variants from atomically resolved STEM images
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Valleti, S. M. P., Kalinin, Sergei V., Nelson, Christopher T., Peters, Jonathan J. P., Dong, Wen, Beanland, Richard, Zhang, Xiaohang, Takeuchi, Ichiro and Ziatdinov, Maxim (2022) Unsupervised learning of ferroic variants from atomically resolved STEM images. AIP Advances, 12 (10). 105122. doi:10.1063/5.0105406 ISSN 2158-3226.
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Official URL: https://doi.org/10.1063/5.0105406
Abstract
An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE .
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > QC Physics Q Science > QD Chemistry |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | |||||||||||||||
| SWORD Depositor: | Library Publications Router | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Ferromagnetic materials, Ferroelectricity -- Research, Ferroelectric crystals, Nanotechnology, Scanning transmission electron microscopy | |||||||||||||||
| Journal or Publication Title: | AIP Advances | |||||||||||||||
| Publisher: | AIP Publishing | |||||||||||||||
| ISSN: | 2158-3226 | |||||||||||||||
| Official Date: | 1 October 2022 | |||||||||||||||
| Dates: |
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| Volume: | 12 | |||||||||||||||
| Number: | 10 | |||||||||||||||
| Article Number: | 105122 | |||||||||||||||
| DOI: | 10.1063/5.0105406 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Reuse Statement (publisher, data, author rights): | ** Article version: AM ** From Crossref journal articles via Jisc Publications Router ** History: ppub 01-10-2022; issued 01-10-2022. ** Licence for AM version of this article starting on 28-10-2022: https://publishing.aip.org/authors/rights-and-permissions | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 2 December 2022 | |||||||||||||||
| Date of first compliant Open Access: | 2 December 2022 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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