![]() First, we represent crystals by a diffraction image, and then construct a deep-learning neural-network model for classification. Here, we propose a new machine-learning-based approach to automatically classify structures by crystal symmetry. Current methods require a user-specified threshold, and are unable to detect "average symmetries" for defective structures. ![]() A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. The classification accuracy of the structural models of these substances was 72.2% and 76.9%, respectively.Ĭomputational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. Independent deep learning of the network was performed on 100 thousand structural models of the triclinic structure of K4SnO4, generated in several runs of the evolutionary algorithm. The classification criterion was the hit of one or more atoms in their correct crystallographic positions in the structure of the substance. Second, ANN was applied for a similar classification of structural models generated by a stochastic evolutionary algorithm in the search for triclinic crystal structures of test compounds K4SnO4 and Rb4SnO4 from their full-profile diffraction patterns. The accuracy of classification by a network of crystalline systems was 87.9%, and space groups was 77.2%. The ICSD database contains 192004 structures, of which 80% were used for deep network learning, and 20% for independent testing of recognition accuracy. First, ANNs were used to classify crystal systems and space groups of symmetry based on the full-profile diffractograms calculated from the crystal structures of the ICSD 2017 database. Some possibilities of using convolutional artificial neural networks (ANNs) for powder diffraction structural analysis of crystalline substances are investigated. ![]()
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