[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120531-en":3,"doc-seo-120531-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120531,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Prediction of Tc in superconducting materials - A machine learning approach","Predicting whether a material becomes a superconductor is crucial both for understanding its underlying physics and for practical applications. This work applies machine learning to estimate the critical temperature (Tc) of superconductors using two datasets covering physical properties and chemical composition of 21,263 materials. Models including elastic net, linear regression, decision trees, random forests, multilayer perceptrons, and extreme gradient boosting are evaluated. Random forest achieves the best overall results with RMSE of 9.35 K on the chemical dataset, while XGB yields RMSE of 9.97 K on the physical dataset. Tc is estimated from chemical composition and physical properties such as thermal conductivity, atomic radius, valence, electron affinity, and atomic mass.","RESEARCH ARTICLE | OCTOBER 01 2025  \nPrediction of Tc in superconducting materials: A machine learning approach  \nKiran Nyaupane  ; Poshan Belbase  ; Narayan Prasad Adhikari 􀀧   \nAIP Advances 15, 105001 (2025)  \n[https://doi.org/10.1063/5.0282713](https://doi.org/10.1063/5.0282713)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nArticles You May Be Interested In  \nPrediction of binding energy using machine learning approach  \nAIP Advances (October 2024)  \nInterpretably learning the critical temperature of superconductors: Electron concentration and feature dimensionality reduction  \nAPL Mater. (April 2024)  \nA machine learning approach for the assessment of road traffic noise: Comparison of regressors  \nAIP Conf. Proc. (May 2025)  \nAIP Advances ARTICLE  \n[pubs.aip.org/aip/adv](pubs.aip.org/aip/adv)  \nPrediction of Tc in superconducting materials: A machine learning approach  \n\n| Cite as: AIP Advances 15, 105001 (2025); doi: 10. 1063/5.0282713 Submitted: 29 May 2025 • Accepted: 12 September 2025 •\u003Cbr>Published Online: 1 October 2025 |  |  |  |\n| --- | --- | --- | --- |\n| Kiran Nyaupane,1  Poshan Belbase,2  and Narayan Prasad Adhikari1, a)  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Central Department of Physics, Tribhuvan University, Kirtipur, Kathmandu, Nepal\u003Cbr>2 Department of Physics, Amrit Campus, Tribhuvan University, Kathmandu, Nepal\u003Cbr>a)Author to whom correspondence should be addressed: [narayan.adhikari@cdp.tu.edu.np](narayan.adhikari@cdp.tu.edu.np) |  |  |  |\n| ABSTRACT\u003Cbr>Knowing whether a material becomes a superconductor or not is of utmost importance, given the interesting physics behind it and also from a practical point of view. Similarly, predicting the critical temperature of a superconductor is also important. In this work, we carried out a machine learning approach to predict the critical temperature of a superconductor. Different machine learning methods, namely elastic net regressor, linear regressor, decision tree regressor, random forest regressor, multilayer perceptron regressor, and extreme gradient boosting (XGB) regressor, are applied to two different datasets containing physical properties and chemical composition of 21 263 superconductors. The primary objective was to predict the critical temperature of these superconductors by leveraging the aforementioned algorithms and their underlying statistical methodologies. Among the models, the random forest regressor demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 9.35 K on the dataset containing chemical properties. This was closely followed by the XGB regressor, which attained an RMSE of 9.97 K on the physical dataset, with other algorithms exhibiting relatively higher RMSE values. The critical temperatures are estimated based on the chemical composition and physical properties, such as thermal conductivity, atomic radius, valence, electron affinity, and atomic mass, of the materials considered independently.\u003Cbr>© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercialNoDerivs 4.0 International (CC BY-NC-ND) license ([https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)). [https://doi.org/10.1063/5.0282713](https://doi.org/10.1063/5.0282713) |  |  |  |\n\nI. INTRODUCTION  \nSuperconductivity is the property of a material to conduct electricity without the loss of energy when the material is cooled below the critical temperature. It is a completely quantum phenomenon and can be explained with the help of BCS theory,1,2 if it is a conventional superconductor. The expulsion of magnetic fields, also known as the Meissner effect, is the characteristic of superconducting materials that can be observed during the transition of the material to the superconducting state below the critical temperature. The critical temperature of a superconductor is an essential factor for the application of a superconducting material. With the d","cbCaiuJHqkcvt2lv","https://ap.wps.com/l/cbCaiuJHqkcvt2lv","pdf",6564638,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the document’s main goal?\",\"answer\":\"To predict the critical temperature (Tc) of superconducting materials using machine learning models trained on physical and chemical data.\"},{\"question\":\"Which machine learning methods are used for the Tc prediction?\",\"answer\":\"The study evaluates elastic net regressor, linear regressor, decision tree regressor, random forest regressor, multilayer perceptron regressor, and extreme gradient boosting (XGB) regressor.\"},{\"question\":\"Which model performs best and what accuracy is reported?\",\"answer\":\"Random forest regressor performs best, achieving an RMSE of 9.35 K on the dataset with chemical properties; XGB is close behind with RMSE of 9.97 K on the physical dataset.\"}]","Prediction of Tc in superconducting materials - A machine learning approach | PDF",1785730516,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-tc-in-superconducting-materials-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-tc-in-superconducting-materials-a-machine-learning-approach/120531/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the document’s main goal?","Question",{"text":75,"@type":76},"To predict the critical temperature (Tc) of superconducting materials using machine learning models trained on physical and chemical data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for the Tc prediction?",{"text":80,"@type":76},"The study evaluates elastic net regressor, linear regressor, decision tree regressor, random forest regressor, multilayer perceptron regressor, and extreme gradient boosting (XGB) regressor.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and what accuracy is reported?",{"text":84,"@type":76},"Random forest regressor performs best, achieving an RMSE of 9.35 K on the dataset with chemical properties; XGB is close behind with RMSE of 9.97 K on the physical dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]