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However, the grand challenge\u2014the missing\u2010wedge\u2010induced information loss and artifacts\u2014has greatly hindered them from obtaining 3D atomic structures with high contrast, high precision, and high fidelity. Herein, for the first time, by combining atomic electron tomography with an artificially intelligent \u201cdeepfake\u201d neural network, this work demonstrates that the resolution of 3D imaging can be improved down to 0.71\u2009\u00c5, which is a record high resolution achieved by electron tomography. It is also shown that the lost information in reconstructed tomograms can be effectively recovered by only acquiring data from \u221250 to +50\u2009\u00b0 (44% reduction of dosage compared with \u221290 to +90\u2009\u00b0 full tilt series). In contrast to conventional methods, the deep\u2010learning model shows outstanding performance for both macroscopic objects and atomic features solving the long\u2010standing dosage and missing\u2010wedge problems in electron tomography. This work provides important guidance for the application of machine learning methods to tomographic imaging atomic\u2010scale features in nanomaterials.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202000152","type":"journal-article","created":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T06:58:34Z","timestamp":1600844314000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["0.7\u2009\u00c5 Resolution Electron Tomography Enabled by Deep\u2010Learning\u2010Aided Information Recovery"],"prefix":"10.1002","volume":"2","author":[{"given":"Chunyang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Physics and Astronomy University of California  Irvine CA 92697 USA"}]},{"given":"Guanglei","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy University of California  Irvine CA 92697 USA"},{"name":"School of Information and Communication Engineering Beijing University of Posts and Telecommunications  Beijing 100876 China"}]},{"given":"Yitong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering Beijing University of Posts and Telecommunications  Beijing 100876 China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6521-868X","authenticated-orcid":false,"given":"Huolin L.","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy University of California  Irvine CA 92697 USA"}]}],"member":"311","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"e_1_2_5_2_1","doi-asserted-by":"publisher","DOI":"10.1557\/mrs.2020.90"},{"key":"e_1_2_5_3_1","doi-asserted-by":"crossref","first-page":"xiv, 455","DOI":"10.1007\/978-0-387-69008-7","volume-title":"Electron Tomography: Methods for Three-Dimensional Visualization of Structures in the Cell","author":"Frank J.","year":"2006"},{"key":"e_1_2_5_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/217130a0"},{"key":"e_1_2_5_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3991(03)00105-0"},{"key":"e_1_2_5_6_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.120.186102"},{"key":"e_1_2_5_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmat2406"},{"key":"e_1_2_5_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/anie.201401059"},{"key":"e_1_2_5_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms13335"},{"key":"e_1_2_5_10_1","doi-asserted-by":"publisher","DOI":"10.1021\/ma902035p"},{"key":"e_1_2_5_11_1","doi-asserted-by":"crossref","first-page":"2410","DOI":"10.1007\/s11661-016-3380-3","volume":"47","author":"Song M.","year":"2016","journal-title":"Metall. 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