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  Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

Li, Y., Zhou, X., Colnaghi, T., Wei, Y., Marek, A., Li, H., et al. (2021). Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys. npj Computational Materials, 7(1): 8. doi:10.1038/s41524-020-00472-7.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-B685-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-B691-7
Genre: Journal Article

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 Creators:
Li, Yue1, Author              
Zhou, Xuyang1, Author              
Colnaghi, Timoteo2, Author              
Wei, Ye3, Author              
Marek, Andreas4, Author              
Li, Hongxiang5, Author              
Bauer, Stefan6, Author              
Rampp, Markus4, Author              
Stephenson, Leigh1, Author              
Affiliations:
1Atom Probe Tomography, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863384              
2Max Planck Computing and Data Facility, Max Planck Society, ou_2364734              
3Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
4Computer Center Garching (RZG), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856297              
5State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing, 100083, China, ou_persistent22              
6Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Atoms; Convolution; Face recognition; Matrix algebra; Nanotechnology, Atom-probe tomography; Atomic configuration; Crystallographic information; Face centered cubic (fcc) alloys; Hardening capacities; Short-range ordered; Spatial distribution map; Threedimensional (3-d), Convolutional neural networks
 Abstract: Nanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (gt;10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. © 2021, The Author(s).

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Language(s): eng - English
 Dates: 2021-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41524-020-00472-7
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Title: npj Computational Materials
  Abbreviation : npj Comput. Mater.
Source Genre: Journal
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Publ. Info: London : Springer Nature
Pages: - Volume / Issue: 7 (1) Sequence Number: 8 Start / End Page: - Identifier: ISSN: 2057-3960
CoNE: https://pure.mpg.de/cone/journals/resource/2057-3960