Autoencoder latent space sensitivity to material structure in convergent-beam low energy electron diffraction

Ivanov, M. and Pereiro, J. ORCID: https://orcid.org/0000-0002-2582-3498 2024. Autoencoder latent space sensitivity to material structure in convergent-beam low energy electron diffraction. Ultramicroscopy 266 , 114021. 10.1016/j.ultramic.2024.114021

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Abstract

The convergent-beam low energy electron diffraction technique has been proposed as a novel method to gather local structural and electronic information from crystalline surfaces during low-energy electron microscopy. However, the approach suffers from high complexity of the resulting diffraction patterns. We show that Convolutional Autoencoders trained on CBLEED patterns achieve a highly structured latent space. The latent space is then used to estimate structural parameters with sub-angstrom accuracy. The low complexity of the neural networks enables real time application of the approach during experiments with low latency.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Physics and Astronomy
Publisher:	Elsevier
ISSN:	0304-3991
Funders:	EPSRC / ERC
Date of First Compliant Deposit:	28 August 2024
Date of Acceptance:	1 August 2024
Last Modified:	19 Sep 2024 09:53
URI:	https://orca.cardiff.ac.uk/id/eprint/171625

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