![]() X-ray Scattering Image Classification Using Deep Learning. Powder Diffraction Indexing as a Pattern Recognition Problem: A New Approach for Unit Cell Determination Based on an Artificial Neural Network. Feasibility study with calculated powder patterns. Quantitative phase analysis of clay minerals by X-ray powder diffraction using artificial neural networks. Artificial neural network methods for the prediction of framework crystal structures of zeolites from XRD data. The Rietveld Method ( Oxford University Press. Billinge (ed.) Powder Diffraction Theory and Practice ( Royal Society of Chemistry, 2008). The classification accuracy of the structural models of these substances was 72.2% and 76.9%, respectively. Independent deep learning of the network was performed on 100 thousand structural models of the triclinic structure of K 4SnO 4, 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 K 4SnO 4 and Rb 4SnO 4 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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