Open AccessClassification of grazing‐incidence small‐angle X‐ray scattering patterns by convolutional neural network
Author(s) -
Ikemoto Hiroyuki,
Yamamoto Kazushi,
Touyama Hideaki,
Yamashita Daisuke,
Nakamura Masataka,
Okuda Hiroshi
Publication year - 2020
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s1600577520005767
Subject(s) - grazing incidence small angle scattering , convolutional neural network , scattering , small angle x ray scattering , incidence (geometry) , set (abstract data type) , computer science , artificial neural network , optics , materials science , artificial intelligence , pattern recognition (psychology) , physics , small angle neutron scattering , neutron scattering , programming language
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.