Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook-Reference-Cited by-同舟云学术

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

Published:2022 Issue:12 Volume:7 Page:1427-1477
ISSN:2055-6756
Container-title:Nanoscale Horizons
language:en
Short-container-title:Nanoscale Horiz.

Author:

Botifoll Marc¹^ORCID,Pinto-Huguet Ivan¹,Arbiol Jordi¹²^ORCID

Affiliation:

1. Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain

2. ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain

Abstract

The cross-fertilisation between the listed disciplines with a long standing knowledge on the application of artificial intelligence protocols and electron microscopy for materials science can entail the next breakthroughs in the field.

Funder

Consejo Superior de Investigaciones Científicas

Agència de Gestió d’Ajuts Universitaris i de Recerca

European Regional Development Fund

Agencia Estatal de Investigación

Generalitat de Catalunya

Ministerio de Ciencia e Innovación

European Commission

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/NH/D2NH00377E

Reference670 articles.

1. Smart Align—a new tool for robust non-rigid registration of scanning microscope data

2. StatSTEM: An efficient approach for accurate and precise model-based quantification of atomic resolution electron microscopy images

3. A.De Backer , S.Van Aert , P. D.Nellist and L.Jones , Procedure for 3D atomic resolution reconstructions using atom-counting and a Bayesian genetic algorithm , arXiv, 2021, preprint, arXiv.2105.05562, 10.48550/arXiv.2105.05562

4. Atom counting in HAADF STEM using a statistical model-based approach: Methodology, possibilities, and inherent limitations

5. Strategy for reliable strain measurement in InAs/GaAs materials from high-resolution Z-contrast STEM images

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