Classification of crystal structure using a convolutional neural network

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as...

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Vydáno v:IUCrJ Ročník 4; číslo 4; s. 486 - 494
Hlavní autoři: Park, Woon Bae, Chung, Jiyong, Jung, Jaeyoung, Sohn, Keemin, Singh, Satendra Pal, Pyo, Myoungho, Shin, Namsoo, Sohn, Kee-Sun
Médium: Journal Article
Jazyk:angličtina
Vydáno: England International Union of Crystallography 01.07.2017
Témata:
artificial neural network (ANN)
Artificial neural networks
computational modelling
convolutional neural network (CNN)
Crystal structure
crystal structure prediction
crystal system
inorganic materials
Observations
powder X-ray diffraction
properties of solids
Research Papers
crystal system
artificial neural network (ANN)
inorganic materials
properties of solids
convolutional neural network (CNN)
crystal structure prediction
computational modelling
powder X-ray diffraction
ISSN:2052-2525, 2052-2525
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Popis
Shrnutí:A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. 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. 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. The CNN interprets features that humans cannot recognize in a powder XRD pattern. 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 well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.
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These authors contributed equally.
ISSN:2052-2525
2052-2525
DOI:10.1107/S205225251700714X