{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:41:30Z","timestamp":1782517290677,"version":"3.54.5"},"reference-count":32,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22225606"],"award-info":[{"award-number":["22225606"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22379021"],"award-info":[{"award-number":["22379021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22361142703"],"award-info":[{"award-number":["22361142703"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>\n                    In experimental catalysis research, conventional trial\u2010and\u2010error approach still dominates during the exploration of the intricate relationship between microscopic mechanisms and macroscopic performance, which unfortunately is time\u2010consuming and resource\u2010intensive, placing additional hurdles on catalyst high\u2010throughput screening. Alternatively, machine learning (ML) methods offer new possibilities to largely accelerate the entire process due to their capability of extracting hidden patterns. Herein, we exploit machine\u2010learnt vibrational spectroscopy to establish a direct, predictable, interpretable, and transferable relationship between infrared (IR) signals of adsorption with catalytic performance. Taking photocatalytic NO oxidation reaction as a typical case, the framework is shown to effectively and accurately predict the nitrate formation based solely on the IR spectral signals of NO adsorption, the generalizability of which is additionally demonstrated with a new CaCO\n                    <jats:sub>3<\/jats:sub>\n                    \u2010decorated g\u2010C\n                    <jats:sub>3<\/jats:sub>\n                    N\n                    <jats:sub>4<\/jats:sub>\n                    system. The physical sanity of this model can be rationalized by the extracted insights well\u2013aligned with mechanistic understandings, and its transferability is illustrated with a further prediction on NO removal activity. The ML\u2010based approach reduces experiment time by 3.5 times and enables quantitative determination of catalytic performances, thereby expanding the applicability of traditional spectroscopic techniques for catalyst screening and prediction.\n                  <\/jats:p>","DOI":"10.1002\/aisy.202500101","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T05:30:09Z","timestamp":1750051809000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Infrared Spectra\u2010Based Machine Learning Framework for Photocatalytic Reaction and Performance"],"prefix":"10.1002","volume":"7","author":[{"given":"Yanxia","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environment University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Resources and Environment University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environment University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environment University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Quantum Physics and Photonic Quantum Information Ministry of Education Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2890-9964","authenticated-orcid":false,"given":"Fan","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Resources and Environment University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"},{"name":"Research Center for Carbon\u2010Neutral Environmental &amp; Energy Technology Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China  Chengdu 611731 P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aav4278"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nchem.2607"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8415"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.9b04956"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03382-w"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.125.085503"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1165893"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/celc.202300647"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seppur.2024.128695"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.adn2777"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.4c09363"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms15438"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.4c09079"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-018-0096-5"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.3c09299"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwae389"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.4c12174"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.1c00107"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2019.122026"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscatal.2c02326"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscatal.8b00521"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apcatb.2014.10.025"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.estlett.1c00661"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1039\/D1EE02889H"},{"key":"e_1_2_9_26_1","unstructured":"S. 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