{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:58:28Z","timestamp":1785873508442,"version":"3.56.0"},"reference-count":41,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102019R1A2C2002447"],"award-info":[{"award-number":["NRF\u20102019R1A2C2002447"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Small Methods"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Multicomponent oxide systems are one of the essential building blocks in a broad range of electronic devices. However, due to the complex physical correlation between the cation components and their relations with the system, finding an optimal combination for desired physical and\/or chemical properties requires an exhaustive experimental procedure. Here, a machine learning (ML)\u2010based synthetic approach is proposed to explore the optimal combination conditions in a ternary cationic compound indium\u2010zinc\u2010tin oxide (IZTO) semiconductor exhibiting high carrier mobility. In particular, by using support vector regression algorithm with radial basis function kernel, highly accurate mobility prediction can be achieved for multicomponent IZTO semiconductor with a sufficiently small number of train datasets (15\u201320 data points). With a synergetic combination of solution\u2010based synthetic route for IZTO fabrication enabling a facile control of the composition ratio and tailored ML process for multicomponent system, the prediction of high\u2010performance IZTO thin\u2010film transistors is possible with expected field\u2010effect mobility as high as 13.06 cm<jats:sup>2<\/jats:sup> V<jats:sup>\u22121<\/jats:sup> s<jats:sup>\u22121<\/jats:sup> at the In:Zn:Sn ratio of 63:27:10. The ML prediction is successfully translated into the empirical analysis with high accuracy, validating the protocol is reliable and a promising approach to accelerate the optimization process for multicomponent oxide systems.<\/jats:p>","DOI":"10.1002\/smtd.202101293","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T06:00:30Z","timestamp":1637128830000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Highly Reliable Implementation of Optimized Multicomponent Oxide Systems Enabled by Machine Learning\u2010Based Synthetic Protocol"],"prefix":"10.1002","volume":"5","author":[{"given":"Boyeon","family":"Park","sequence":"first","affiliation":[{"name":"School of Advanced Materials Science and Engineering Sungkyunkwan University  Suwon 16419 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minho","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Advanced Materials Science and Engineering Sungkyunkwan University  Suwon 16419 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngjin","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Advanced Materials Science and Engineering Sungkyunkwan University  Suwon 16419 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hun\u2010Bum","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering Chung\u2010Ang University  Seoul 06974 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung\u2010Gil","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Advanced Materials Science and Engineering Sungkyunkwan University  Suwon 16419 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung Kyu","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering Chung\u2010Ang University  Seoul 06974 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0057-1893","authenticated-orcid":false,"given":"Yong\u2010Hoon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Advanced Materials Science and Engineering Sungkyunkwan University  Suwon 16419 Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"e_1_2_8_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/LED.2014.2381631"},{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsami.5b12726"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","first-page":"28.2.1","DOI":"10.1109\/IEDM13553.2020.9371900","volume-title":"2020 IEEE Int. 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