11/24/2023 0 Comments Lattice pattern cnnThis complication is the reason that the crystal symmetry (space group) cannot be obtained correctly from a powder XRD pattern for many low-symmetry phases, no matter what type of measurement tool is employed. Such data condensation leads to both accidental and exact peak overlap, which complicates the determination of individual peak intensities. It would be a very difficult to describe an actual crystal structure perfectly using only powder X-ray diffraction (XRD) patterns as the raw data source, because the three-dimensional electron-density distribution is condensed into just one dimension in the powder diffraction pattern. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The CNN interprets features that humans cannot recognize in a powder XRD pattern. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. A deep machine-learning technique based on a convolutional neural network (CNN) is introduced.
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