Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds | Научно-инновационный портал СФУ

Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds

Тип публикации: статья из журнала

Год издания: 2019

Идентификатор DOI: 10.17516/1998-2836-0118

Ключевые слова: crystal structure, powder diffraction, genetic algorithms, Rietveld method, artificial neural networks

Аннотация: Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calc Some possibilities of using convolutional artificial neural networks (ANN) for powder diffraction structural analysis of crystalline substances have been investigated. First, ANNs are used to classify crystalline systems and space groups according to calculated full-profile diffractograms calculated from the crystal structures of the ICSD database (2017 year). The ICSD database contains 192004 structures, of which 80% was used for in-depth network training, and 20% for independent testing of recognition accuracy. The accuracy of classification by a network of crystalline systems was 87.9%, and that of space groups was 77.2%. Secondly, the ANN is used for a similar classification of structural models generated by the stochastic genetic algorithm in the search processes for triclinic crystal structures of test compound K4SnO4 according to their full-profile diffraction patterns. The classification criterion was the entry of one or several atoms into their crystallographic positions in the structure of a substance. Independent deep network training was performed on 120 thousand structural models of the K4PbO4 triclinic structure generated in several runs of the genetic algorithm. The accuracy of the classification of K4SnO4 structural models exceeded 50%. The results show that deeply trained convolutional ANNs can be effective for classifying crystal structures according to the structural characteristics of their powder diffraction patterns. © Siberian Federal University. All rights reserved

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Издание

Журнал: JOURNAL OF SIBERIAN FEDERAL UNIVERSITY-CHEMISTRY

Выпуск журнала: Vol. 12, Is. 2

Номера страниц: 188-200

ISSN журнала: 19982836

Место издания: KRASNOYARSK

Издатель: SIBERIAN FEDERAL UNIV

Авторы

  • Zaloga Alexander N. (Siberian Fed Univ, 79 Svobodny, Krasnoyarsk 660041, Russia)
  • Stanovov Vladimir V. (Siberian Fed Univ, 79 Svobodny, Krasnoyarsk 660041, Russia)
  • Bezrukova Oksana E. (Siberian Fed Univ, 79 Svobodny, Krasnoyarsk 660041, Russia)
  • Dubinin Petr S. (Siberian Fed Univ, 79 Svobodny, Krasnoyarsk 660041, Russia)
  • Yakimov Igor S. (Siberian Fed Univ, 79 Svobodny, Krasnoyarsk 660041, Russia)

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