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

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Zhou,  Xuyang
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Wei,  Ye
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Zhao,  Huan
Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Darvishi Kamachali,  Reza
Theory and Simulation, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Raabe,  Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Gault,  Baptiste
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Imperial College, Royal School of Mines, Department of Materials, London, SW7 2AZ, UK;

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Citation

Zhou, X., Wei, Y., Zhao, H., Vogel, F., Darvishi Kamachali, R., Thompson, G. B., et al. (2021). Revealing in-plane grain boundary composition features through machine learning from atom probe tomography. Talk presented at DPG Conference 2021. Online, Germany. 2021-09-27 - 2021-10-01.


Cite as: https://hdl.handle.net/21.11116/0000-000F-C4B3-9
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