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  3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods

Wei, Y., Peng, Z., Kühbach, M., Breen, A. J., Legros, M., Larranaga, M., et al. (2019). 3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods. PLoS One, 14(11): e0225041. doi:10.1371/journal.pone.0225041.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-426E-6 Version Permalink: https://hdl.handle.net/21.11116/0000-000B-5FF8-4
Genre: Journal Article

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3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods - file.pdf
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 Creators:
Wei, Ye1, Author           
Peng, Zirong1, Author           
Kühbach, Markus2, Author           
Breen, Andrew J.3, Author           
Legros, Marc4, Author           
Larranaga, Melvyn5, Author           
Mompiou, Frederic6, Author           
Gault, Baptiste3, Author           
Affiliations:
1Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
2Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863392              
3Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863384              
4CEMES-CNRS, 29 rue J. Marvig, 31055 Toulouse, France, ou_persistent22              
5CEMES-CNRS, 29 Rue Jeanne-Marvig, Toulouse, France, ou_persistent22              
6CEMES, CNRS, Université de Toulouse, 29 Rue Jeanne Marvig, Toulouse, 31055, France, ou_persistent22              

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Language(s): eng - English
 Dates: 2019-11-18
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pone.0225041
 Degree: -

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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 19 Volume / Issue: 14 (11) Sequence Number: e0225041 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850