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For this purpose, a fully convolutional neural network (FCN), transformer encoder (T\u2010encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well\u2010established crystal graph convolutional neural network (CGCNN). A task\u2010specified small dataset that focuses on a narrow material system, knowledge (rule)\u2010based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T\u2010encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine\u2010learning\u2010driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD\u2010driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well\u2010separated clustering according to the symmetry and property is observed.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200042","type":"journal-article","created":{"date-parts":[[2022,5,22]],"date-time":"2022-05-22T22:25:02Z","timestamp":1653258302000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Powder X\u2010Ray Diffraction Pattern Is All You Need for Machine\u2010Learning\u2010Based Symmetry Identification and Property Prediction"],"prefix":"10.1002","volume":"4","author":[{"given":"Byung Do","family":"Lee","sequence":"first","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"}]},{"given":"Jin-Woong","family":"Lee","sequence":"additional","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"}]},{"given":"Woon Bae","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Advanced Components and Materials Engineering Sunchon National University  Chonnam 57922 South Korea"}]},{"given":"Joonseo","family":"Park","sequence":"additional","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"}]},{"given":"Min-Young","family":"Cho","sequence":"additional","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"}]},{"given":"Satendra","family":"Pal Singh","sequence":"additional","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"},{"name":"Department of Physics University of Lucknow  Lucknow Uttar Pradesh 226007 India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6411-7548","authenticated-orcid":false,"given":"Myoungho","family":"Pyo","sequence":"additional","affiliation":[{"name":"Department of Advanced Components and Materials Engineering Sunchon National University  Chonnam 57922 South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-2283","authenticated-orcid":false,"given":"Kee-Sun","family":"Sohn","sequence":"additional","affiliation":[{"name":"Faculty of Nanotechnology and Advanced Materials Engineering Sejong University  209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea"}]}],"member":"311","published-online":{"date-parts":[[2022,5,22]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"Powder Diffraction: Theory and Practice","author":"Dinnebier R. 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