{"id":"doi:10.1088/2632-2153/ae04c1","kind":"article","title":"High- T c superconductor candidates proposed by machine learning","authors":["Siwoo Lee","Jason R. Hattrick-Simpers","Young‐June Kim","O. Anatole von Lilienfeld"],"author_count":4,"year":2025,"published":"2025-09-08","venue":"Machine Learning Science and Technology","cited_by":2,"doi":"10.1088/2632-2153/ae04c1","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1088/2632-2153/ae04c1","archive_url":"https://space-kitty.com/papers/doi:10.1088/2632-2153/ae04c1","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4414050276","pdf_url":null,"abstract":"Abstract We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature ( T c ) as a statistical learning problem with reduced complexity. Training of query-aware similarity-based ridge regression models on experimental SuperCon data achieves average T c prediction errors of ±5 K for unseen out-of-sample materials. Two models were trained with one excluding high pressure data in training (‘ambient’ model) and a second also including high pressure data (‘implicit’ model). Subsequent utilization of the approach to scan ∼153 k materials in the Materials Project enables the ranking of candidates by T c while accounting for thermodynamic stability and small band gap. The ambient model is used to predict stable top three high- T c candidate materials that include those with large band gaps of LiCuF 4 (316 K), Ag 2 H 12 S(NO) 4 (316 K), and Na 2 H 6 PtO 6 (315 K). Filtering these candidates for those with small band gaps correspondingly yields LiCuF 4 (316 K), Cu 2 P 2 O 7 (311 K), and Cu 3 P 2 H 2 O 9 (307 K).","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"High- T c superconductor candidates proposed by machine learning\" by Siwoo Lee, Jason R. Hattrick-Simpers, Young‐June Kim et al., https://doi.org/10.1088/2632-2153/ae04c1, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"superconductor-discovery","name":"Computational & AI-Driven Superconductor Discovery","domain_slug":"superconductivity"}],"cited_in_dossiers":[]}