{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-modeling-of-superconducting","title":"Machine learning modeling of superconducting critical temperature","arxiv_id":"1709.02727","date":"2017-09-08","proceeding":null,"authors":["Valentin Stanev","Corey Oses","A. Gilad Kusne","Efrain Rodriguez","Johnpierre Paglione","Stefano Curtarolo","Ichiro Takeuchi"],"abstract":"Superconductivity has been the focus of enormous research effort since its\ndiscovery more than a century ago. Yet, some features of this unique phenomenon\nremain poorly understood; prime among these is the connection between\nsuperconductivity and chemical/structural properties of materials. To bridge\nthe gap, several machine learning schemes are developed herein to model the\ncritical temperatures ($T_{\\mathrm{c}}$) of the 12,000+ known superconductors\navailable via the SuperCon database. Materials are first divided into two\nclasses based on their $T_{\\mathrm{c}}$ values, above and below 10 K, and a\nclassification model predicting this label is trained. The model uses\ncoarse-grained features based only on the chemical compositions. It shows\nstrong predictive power, with out-of-sample accuracy of about 92%. Separate\nregression models are developed to predict the values of $T_{\\mathrm{c}}$ for\ncuprate, iron-based, and \"low-$T_{\\mathrm{c}}$\" compounds. These models also\ndemonstrate good performance, with learned predictors offering potential\ninsights into the mechanisms behind superconductivity in different families of\nmaterials. To improve the accuracy and interpretability of these models, new\nfeatures are incorporated using materials data from the AFLOW Online\nRepositories. Finally, the classification and regression models are combined\ninto a single integrated pipeline and employed to search the entire Inorganic\nCrystallographic Structure Database (ICSD) for potential new superconductors.\nWe identify more than 30 non-cuprate and non-iron-based oxides as candidate\nmaterials.","url_abs":"http://arxiv.org/abs/1709.02727v2","url_pdf":"http://arxiv.org/pdf/1709.02727v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-modeling-of-superconducting","repo_url":"https://github.com/robertvici/Predicting-the-Critical-Temperature-of-a-Superconductor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.02727","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}