Classification of grazing‐incidence small‐angle X‐ray scattering patterns by convolutional neural network

• Published in: Journal of synchrotron radiation Vol. 27; no. 4; pp. 1069 - 1073
• Main Authors: Ikemoto, Hiroyuki, Yamamoto, Kazushi, Touyama, Hideaki, Yamashita, Daisuke, Nakamura, Masataka, Okuda, Hiroshi
• Format: Journal Article
• Language: English
• Published: 5 Abbey Square, Chester, Cheshire CH1 2HU, England International Union of Crystallography 01.07.2020; John Wiley & Sons, Inc
• Subjects: Artificial neural networks; Classification; convolutional neural network; deep learning; GISAXS; Grazing; Incidence; Nanoparticles; Neural networks; Scattering; GISAXS; deep learning; convolutional neural network
• ISSN: 1600-5775; 0909-0495; 1600-5775
• DOI: 10.1107/S1600577520005767

Grazing‐incidence small‐angle X‐ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations. Convolutional neural networks are useful for classifying grazing‐incidence small‐angle X‐ray scattering patterns. They are also useful for classifying real experimental data.