Direct recognition of crystal structures via three-dimensional convolutional 
neural networks with high accuracy and tolerance to random displacements and 
missing atoms

Rao, Ziyuan; Li, Yue; Zhang, Hongbin; Colnaghi, Timoteo; Marek, A.; Rampp, Markus; Gault, Baptiste

doi:10.1016/j.scriptamat.2023.115542

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Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms

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Rao, Ziyuan
De magnete - Designing Magnetism on the atomic scale, MPG Group, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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

/persons/resource/persons109883

Marek, A.
Max Planck Computing and Data Facility, Max Planck Society;

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Rampp, Markus
Max Planck Computing and Data Facility, 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

Rao, Z., Li, Y., Zhang, H., Colnaghi, T., Marek, A., Rampp, M., et al. (2023). Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms. Scripta Materialia, 234: 115542. doi:10.1016/j.scriptamat.2023.115542.

Cite as: https://hdl.handle.net/21.11116/0000-000D-5663-3

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