Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds
Alexander N. Zaloga
Vladimir V. Stanovov
Oksana E. Bezrukova
Petr S. Dubinin
Igor S. Yakimov
Институт цветных металлов и материаловедения
Кафедра композиционных материалов и физико-химии металлургических процессов
Journal Name:Journal of Siberian Federal University. Chemistry
Journal Quartile in Web of Science:без квартиля
Bibliographic Citation:Alexander N. Zaloga. Possibilities of Neural Network Powder Diffraction Analysis Crystal Structure of Chemical Compounds [Текст] / Alexander N. Zaloga, Vladimir V. Stanovov, Oksana E. Bezrukova, Petr S. Dubinin, Igor S. Yakimov // Journal of Siberian Federal University. Chemistry. — 2019.
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.