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

التفاصيل البيبلوغرافية
العنوان: Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
المؤلفون: Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek, Hongxiang Li, Stefan Bauer, Markus Rampp, Leigh T. Stephenson
المصدر: npj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021)
بيانات النشر: Nature Portfolio, 2021.
سنة النشر: 2021
المجموعة: LCC:Materials of engineering and construction. Mechanics of materials
LCC:Computer software
مصطلحات موضوعية: Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
الوصف: 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 (>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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2057-3960
Relation: https://doaj.org/toc/2057-3960
DOI: 10.1038/s41524-020-00472-7
URL الوصول: https://doaj.org/article/8891f358ab0b465da127046801a0859a
رقم الأكسشن: edsdoj.8891f358ab0b465da127046801a0859a
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20573960
DOI:10.1038/s41524-020-00472-7