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This study introduces a neural network\u2010empowered smart scan technique that achieves a relative increase in speed compared to traditional energy\u2010dispersive X\u2010ray spectroscopy (EDX) mapping. The main advantage is that it reduces the required dose, decreasing potential damage to the sample by avoiding unnecessary exposure. It holds potential use in other multimodal scanning transmission electron microscopy or scanning\u2010based imaging approaches. In the first example, identifying particles in a matrix with a trained neural network reduces the acquisition time by two orders of magnitude. This acceleration enables a statistical compositional analysis of thousands of particles in less than 1\u2009h. Similar improvements are observed for atomic resolution. The discrete positions of atoms identified by the trained network allow for selective EDX sampling at these centers, thereby identifying the atomic species of the column with much\u2010reduced sampling. Consequently, a lower sampling dose is required, enabling mapping of more delicate materials with high lateral resolution and at a high statistical confidence interval. Even though manual training is still required, this approach greatly benefits repetitive quality control tasks.<\/jats:p>","DOI":"10.1002\/aisy.202300745","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T06:35:11Z","timestamp":1721198111000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence\u2010Driven Smart Scan: A Rapid, Automatic Approach for Comprehensive Imaging and Spectroscopy for Fast Compositional Analysis"],"prefix":"10.1002","volume":"6","author":[{"given":"Pavel","family":"Potocek","sequence":"first","affiliation":[{"name":"Thermo Fisher Scientific  Achtseweg Noord 5 5651GG Eindhoven Netherlands"},{"name":"Saarland University  66123 Saarbr\u00fccken Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3059-0852","authenticated-orcid":false,"given":"Cigdem","family":"Ozsoy\u2010Keskinbora","sequence":"additional","affiliation":[{"name":"Thermo Fisher Scientific  Achtseweg Noord 5 5651GG Eindhoven Netherlands"}]},{"given":"Philipp","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"BASF SE  Carl\u2010Bosch\u2010Str. 38 67056 Ludwigshafen am Rhein Germany"}]},{"given":"Thorsten","family":"Wieczorek","sequence":"additional","affiliation":[{"name":"BASF SE  Carl\u2010Bosch\u2010Str. 38 67056 Ludwigshafen am Rhein Germany"}]},{"given":"Maurice","family":"Peemen","sequence":"additional","affiliation":[{"name":"Thermo Fisher Scientific  Achtseweg Noord 5 5651GG Eindhoven Netherlands"}]},{"given":"Philipp","family":"Slusallek","sequence":"additional","affiliation":[{"name":"Saarland University  66123 Saarbr\u00fccken Germany"},{"name":"German Research Center for Artificial Intelligence (DFKI)  Kaiserslautern 67663 Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9228-1182","authenticated-orcid":false,"given":"Bert","family":"Freitag","sequence":"additional","affiliation":[{"name":"Thermo Fisher Scientific  Achtseweg Noord 5 5651GG Eindhoven Netherlands"}]}],"member":"311","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1039\/D2NH00377E"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultramic.2019.03.017"},{"key":"e_1_2_7_4_1","volume-title":"Advances in Electron Microscopy with Deep Learning","author":"Ede J. 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