X-ray Diffraction Data Analysis by Machine Learning Methods—a review
Table 1. Attributes groups to be identified from synchrotron data streams images. Data from reference [ 91 ]. Group Number
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Table 1.
Attributes groups to be identified from synchrotron data streams images. Data from reference [ 91 ]. Group Number Group Attributes Labels G1 Experiments GIWAXS, GISAXS, TSAXS, TWAXS, GTSAXS, Theta sweep, and phi sweep. G2 Instrumentation Beam off image, photonics CCD, MarCCD, Linear beamstop, saturation, asymmetric (left/right), and circular beamstop. G3 Imaging Specular rod, weak scattering, 2D detector obstruction, strong scattering, saturation artifacts, misaligned, beam streaking, blocked, bad beam shape, direct, object obstruction, empty cell, parasitic slit scattering, and point detector obstruction. G4 Scattering Features Horizon, peaks: isolated, ring: oriented z, halo: isotropic, ring: isotropic, ring: textured, higher orders: 2 to 3, ring: oriented xy, vertical streaks, peaks: many/field, diffuse high-q: isotropic, higher orders: 4 to 6, higher orders: 7 to 10, Bragg rods, ring: anisotropic, peaks: along ring, diffuse low-q: isotropic, Yoneda, halo: oriented z, high background, ring: spotted, peak: line Z, peaks: line xy, diffuse low-q: anisotropic, many rings, diffuse low-q: oriented z, diffuse low-q: oriented xy, diffuse specular rod, smeared horizon, symmetry ring: 4-fold, higher orders: 10 to 20, ring doubling, halo: anisotropic, specular rod peaks, ring: oriented other, peaks: line, diffuse high-q: oriented z, peak doubling, halo: oriented xy, diffuse high-q: oriented xy, peaks: line other, waveguide streaks, higher orders: 20 or more, substrate streaks/Kikuchi, diffuse low-q: oriented other, halo: spotted, diffuse low-q: spotted, and diffuse high-q: spotted. G5 Samples Thin film, ordered, single crystal, grating, amorphous, composite, nanoporous, powder, and polycrystalline. G6 Materials Polymer, block–copolymer, and superlattice. G7 Specific Substances P3HT, SiO 2 , PCBM, rubrene, PS-PMMA, silicon, MWCNT, PDMS, AgBH, and LaB 6 . The detection of synchrotron image features was also studied by Czyzewski et al. [ 92 ], which aimed to identify seven types of flaws: ice rings, diffuse scattering, background rings, nonuniform detector responses, loop scattering, strong background, and digital artefacts. The group compared several algorithms (SVM; naïve Bayes—NB; k-nearest neighbors— KNNs; random forest—RF) with CNN, in which they used different inputs (cartesian coordinates and polar coordinates with different interpolation methods: min and max). The dataset used in the study comprised 6311 diffraction images from the Integrated Resource for Reproducibility in Macromolecular Crystallography, from which 5048 were used as a training set, 631 were used as a validation set, and, subsequently, 632 were used in the testing set. The accuracy of class-specific predictive performance for the different classifier algorithms is shown in Table 2 . Their results clearly show that CNN performance was consistently better than any other of the tested classifiers. Moreover, it is worth mentioning that the differences among the CNNs were generally approximately 0.01–0.02. |
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