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Machine-learning-assisted revealing L12 ordered structures in FCC-based alloys

MPS-Authors
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Li,  Yue
Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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

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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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Colnaghi,  Timoteo
Max Planck Computing and Data Facility, Max Planck Society;

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Marek,  Andreas
Max Planck Computing and Data Facility, Max Planck Society;

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Bauer,  Stefan
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Rampp,  Markus
Max Planck Computing and Data Facility, 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;

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Citation

Li, Y., Stephenson, L., Zhou, X., Colnaghi, T., Marek, A., Bauer, S., et al. (2020). Machine-learning-assisted revealing L12 ordered structures in FCC-based alloys. Poster presented at BIGmax workshop 2020, online, Düsseldorf, Germany.


Cite as: http://hdl.handle.net/21.11116/0000-0006-C3CF-5
Abstract
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