{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:25:36Z","timestamp":1786976736318,"version":"3.56.0"},"reference-count":27,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T00:00:00Z","timestamp":1703376000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p>New materials are frequently synthesized and optimized with the explicit intention to improve their properties to meet the ever\u2010increasing societal requirements for high\u2010performance and energy\u2010efficient electronics, new battery concepts, better recyclability, and low\u2010energy manufacturing processes. This often involves exploring vast combinations of stoichiometries and compositions, a process made more efficient by high\u2010throughput robotic platforms. Nonetheless, subsequent analytical methods are essential to screen the numerous samples and identify promising material candidates. X\u2010ray diffraction is a commonly used analysis method available in most laboratories which gives insight into the crystalline structure and reveals the presence of phases in a powder sample. Herein, a method for automating the analysis of XRD patterns, which uses a neural network model to classify samples into nondiffracting, single\u2010phase, and multi\u2010phase structures, is presented. To train neural networks for identifying materials with compositions not matching known crystallographic structures, a synthetic data generation approach is developed. The application of the neural networks on high\u2010entropy oxides experimental data is demonstrated, where materials frequently deviate from anticipated structures. Our approach, not limited to these materials, seamlessly integrates into high\u2010throughput data analysis pipelines, either filtering acquired patterns or serving as a standalone method for automated material exploration workflows.<\/jats:p>","DOI":"10.1002\/aisy.202300501","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T23:17:17Z","timestamp":1703459837000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Accelerating Materials Discovery: Automated Identification of Prospects from X\u2010Ray Diffraction Data in Fast Screening Experiments"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5816-4866","authenticated-orcid":false,"given":"Jan","family":"Schuetzke","sequence":"first","affiliation":[{"name":"Institute for Automation and Applied Informatics Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Schweidler","sequence":"additional","affiliation":[{"name":"Institute of Nanotechnology Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Friedrich R.","family":"Muenke","sequence":"additional","affiliation":[{"name":"Institute for Automation and Applied Informatics Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andre","family":"Orth","sequence":"additional","affiliation":[{"name":"Institute for Automation and Applied Informatics Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anurag D.","family":"Khandelwal","sequence":"additional","affiliation":[{"name":"Institute of Nanotechnology Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ben","family":"Breitung","sequence":"additional","affiliation":[{"name":"Institute of Nanotechnology Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Aghassi\u2010Hagmann","sequence":"additional","affiliation":[{"name":"Institute of Nanotechnology Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Reischl","sequence":"additional","affiliation":[{"name":"Institute for Automation and Applied Informatics Karlsruhe Institute of Technology  Hermann\u2010von\u2010Helmholtz Platz 1 76344 Eggenstein\u2010Leopoldshafen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-08-100040-3.00004-3"},{"key":"e_1_2_7_3_1","volume-title":"Fundamentals of Powder Diffraction and Structural Characterization of Materials","author":"Pecharsky V.","year":"2005"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.268.5218.1738"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/natrevmats.2017.5"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1107\/S0108768102006948"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1107\/S0021889809016690"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202102301"},{"key":"e_1_2_7_9_1","unstructured":"J.Chen S. 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