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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Bibliographic Details
Published in:IUCrJ Vol. 4; no. 4; pp. 486 - 494
Main Authors: Park, Woon Bae, Chung, Jiyong, Jung, Jaeyoung, Sohn, Keemin, Singh, Satendra Pal, Pyo, Myoungho, Shin, Namsoo, Sohn, Kee-Sun
Format: Journal Article
Language:English
Published: England International Union of Crystallography 01.07.2017
Subjects:
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
Online Access:Get full text
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Summary: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