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  Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data

Zhou, X., Wei, Y., Kühbach, M. T., Zhao, H., Vogel, F., Kamachali, R. D., et al. (2022). Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data. Acta Materialia, 226: 117633. doi:10.1016/j.actamat.2022.117633.

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 Creators:
Zhou, Xuyang1, 2, Author              
Wei, Ye3, Author              
Kühbach, Markus Tobias4, 5, Author              
Zhao, Huan6, Author              
Vogel, Florian7, Author              
Kamachali, Reza Darvishi8, Author              
Thompson, Gregory B.9, Author              
Raabe, Dierk10, Author              
Gault, Baptiste1, 11, Author              
Affiliations:
1Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863384              
2The University of Alabama, Department of Metallurgical Materials Engineering, 35487 Tuscaloosa, AL, USA, ou_persistent22              
3Hydrogen in Energy Materials, Project Groups, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3291873              
4Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863392              
5Fritz Haber Institute, Max Planck Society, ou_24021              
6Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863383              
7Institute of Advanced Wear & Corrosion Resistant and Functional Materials, Jinan University, 510632 Guangzhou, China, ou_persistent22              
8Federal Institute for Materials Research and Testing (BAM), Unter den Eichen 87, 12205, Berlin, Germany, ou_persistent22              
9Department of Metallurgical and Materials Engineering, The University of Alabama, Tuscaloosa, AL, USA, ou_persistent22              
10Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
11Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK, ou_persistent22              

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Language(s): eng - English
 Dates: 2022-01-092022-03
 Publication Status: Published in print
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.actamat.2022.117633
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Title: Acta Materialia
  Abbreviation : Acta Mater.
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 226 Sequence Number: 117633 Start / End Page: - Identifier: ISSN: 1359-6454
CoNE: https://pure.mpg.de/cone/journals/resource/954928603100