{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T10:20:01Z","timestamp":1758190801838,"version":"3.44.0"},"reference-count":84,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100010785","name":"Canada First Research Excellence Fund","doi-asserted-by":"crossref","award":["CFREF-2022-00042"],"award-info":[{"award-number":["CFREF-2022-00042"]}],"id":[{"id":"10.13039\/501100010785","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["RGPIN-2023-04853"],"award-info":[{"award-number":["RGPIN-2023-04853"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"Abstract<\/jats:title>\n We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (\n \n \n \n \n T<\/mml:mi>\n \n c<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge regression models on experimental SuperCon data achieves average \n \n \n \n \n T<\/mml:mi>\n \n c<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> prediction errors of \u00b15\u2009K for unseen out-of-sample materials. Two models were trained with one excluding high pressure data in training (\u2018ambient\u2019 model) and a second also including high pressure data (\u2018implicit\u2019 model). Subsequent utilization of the approach to scan \u223c153\u2009k materials in the Materials Project enables the ranking of candidates by \n \n \n \n \n T<\/mml:mi>\n \n c<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> while accounting for thermodynamic stability and small band gap. The ambient model is used to predict stable top three high-\n \n \n \n \n T<\/mml:mi>\n \n c<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula> candidate materials that include those with large band gaps of LiCuF4<\/jats:sub> (316\u2009K), Ag2<\/jats:sub>H12<\/jats:sub>S(NO)4<\/jats:sub> (316\u2009K), and Na2<\/jats:sub>H6<\/jats:sub>PtO6<\/jats:sub> (315\u2009K). Filtering these candidates for those with small band gaps correspondingly yields LiCuF4<\/jats:sub> (316\u2009K), Cu2<\/jats:sub>P2<\/jats:sub>O7<\/jats:sub> (311\u2009K), and Cu3<\/jats:sub>P2<\/jats:sub>H2<\/jats:sub>O9<\/jats:sub> (307\u2009K).<\/jats:p>","DOI":"10.1088\/2632-2153\/ae04c1","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:53:40Z","timestamp":1757372020000},"page":"035052","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["High-\n\t \n\t \n \n \n T<\/mml:mi>\n \n c<\/mml:mi>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n\t \n\t superconductor candidates proposed by machine learning"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-389X","authenticated-orcid":true,"given":"Siwoo","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jason","family":"Hattrick-Simpers","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1172-8895","authenticated-orcid":true,"given":"Young-June","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7419-0466","authenticated-orcid":true,"given":"O","family":"Anatole von Lilienfeld","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"mlstae04c1bib1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1021\/ar00051a003","type":"journal-article","article-title":"Room temperature superconductors","volume":"28","author":"Sleight","year":"1995","journal-title":"Acc. 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