Crystal symmetry classification from powder X-ray diffraction patterns using a convolutional neural network
| Autor: | Igor S. Yakimov, Alexander N. Zaloga, Oksana E. Bezrukova, Vladimir Stanovov, Petr S. Dubinin |
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| Rok vydání: | 2020 |
| Předmět: |
Diffraction
Materials science Artificial neural network business.industry Crystal system Space group Pattern recognition Crystal structure Convolutional neural network Mechanics of Materials X-ray crystallography Materials Chemistry General Materials Science Artificial intelligence Symmetry (geometry) business |
| Zdroj: | Materials Today Communications. 25:101662 |
| ISSN: | 2352-4928 |
| DOI: | 10.1016/j.mtcomm.2020.101662 |
| Popis: | 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 used 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. |
| Databáze: | OpenAIRE |
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