Тип публикации: статья из журнала
Год издания: 2020
Идентификатор DOI: 10.1016/j.mtcomm.2020.101662
Ключевые слова: artificial neural networks, crystal systems, powder diffraction, space groups
Аннотация: A convolutional artificial neural network was applied to identify crystal systems and symmetry space groups by full-profile X-ray diffraction patterns calculated from crystal structures of the ICSD 2017 database. The database contains 192 004 crystal structures; 80 % of them were used as a training dataset, and the other 20 % were Показать полностьюused as a test dataset to establish the accuracy of classification. The neural network identified crystal systems correctly for 90.02 % of structures and space groups for 79.82 % of structures from the test dataset. Factors affecting the classification accuracy were established. The first, nonlinear normalization of intensities of diffraction peaks increases the accuracy, and the second, the accuracy depends on the number of structures represented in each space group. © 2020 Elsevier Ltd
Журнал: Materials Today Communications
Выпуск журнала: Vol. 25
Номера страниц: 101662
ISSN журнала: 23524928