Neural Network Determination of the Crystal Symmetry of Substances Using Powder XRD Patterns Normalized to the Crystal′s Unit Cell Volume : научное издание

Описание

Тип публикации: статья из журнала

Год издания: 2026

Идентификатор DOI: 10.1134/S002247662603008X

Ключевые слова: convolutional neural network, crystal symmetry, powder diffraction, Rietveld analysis for XRD pattern modeling, ICSD Database

Аннотация: Crystal systems, Bravais lattices, extinction groups, space groups, and intervals of unit cell volume are classified using a convolutional neural network based on the deep learning of model full-profile powder XRD patterns computed from ICSD data. A new method is proposed to significantly increase the classification accuracy by norПоказать полностьюmalizing unit cell volumes to the total fixed value when computing the model XRD patterns. The classification accuracy determined from an independent set of normalized model XRD patterns is 97.4% for crystal systems and 88.0% for space groups. Due to its high accuracy, this neural network can be utilized to perform a crystal symmetry analysis using experimental unit cell volumes and to determine space groups without using reflection extinction rules after finding the unit cell volume by commonly utilized indexing programs.<img src="/get_item_image.asp?id=89257106&img=10947_2026_3239_Figa_HTML.png" class="img_big">

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Издание

Журнал: Journal of Structural Chemistry

Выпуск журнала: Т. 67, 3

Номера страниц: 556-570

ISSN журнала: 00224766

Место издания: Moscow

Издатель: Pleiades Publishing, Ltd.

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