[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291082-105":59,"doc-detail-291082-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","tree-models-machine-learning-to-identify-liquid-metal-based-alloy-superconductor-abstract-and-introduction","Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor - Abstract and Introduction","","Superconductors with zero-resistance enable advanced technologies, yet their critical temperature (Tc) is hard to predict and limited by low Tc values. The study applies machine learning to forecast Tc for liquid metal (LM) alloy superconductors using the SuperCon dataset, after preprocessing to address data issues. Tree-based modeling is compared, with Extra Trees achieving R²=0.9519 and RMSE=6.2624 K, then used to screen LM alloys and predict high-Tc candidates across thousands of binary and ternary alloys.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/tree-models-machine-learning-to-identify-liquid-metal-based-alloy-superconductor-abstract-and-introduction/291082/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/tree-models-machine-learning-to-identify-liquid-metal-based-alloy-superconductor-abstract-and-introduction/291082.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address about superconductors?","Question",{"text":112,"@type":113},"Accurate prediction of superconductors’ critical temperature (Tc) remains difficult, and superconductors are also constrained by low Tc, limiting practical advanced applications.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the machine learning model built and evaluated?",{"text":117,"@type":113},"Using the SuperCon dataset, the authors preprocess the data, compare multiple machine learning models, and evaluate performance with R² and RMSE, finding Extra Trees performs best.",{"name":119,"@type":110,"acceptedAnswer":120},"What results are reported for liquid metal alloy superconductors?",{"text":121,"@type":113},"The Extra Trees model is used to predict Tc for liquid metal (LM) alloys, identifying In0.5Sn0.5 as having the highest Tc at 7.01 K, and extending predictions across 2,145 binary and 45,670 ternary alloys with promising outcomes.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291082,1789643398,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor  \nChen Hua 1,2, Jing Liu 1,2,3 *  \n1Key Lab of Cryogenic Science and Technology, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, China.  \n2School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.  \n3School of Biomedical Engineering, Tsinghua University, Beijing 100084, China.  \n*Correspondence: [jliu@mail.ipc.ac.cn](jliu@mail.ipc.ac.cn)  \nAbstract  \nSuperconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the initial endeavor to apply machine learning to predict the critical temperature (Tc) of liquid metal (LM) alloy superconductors. Leveraging the SuperCon dataset, which includes extensive superconductor property data, we developed a machine learning model to predict Tc. After addressing data issues through preprocessing, we compared multiple models and found that the Extra Trees model outperformed others with an R² of 0.9519 and an RMSE of 6.2624 K. This model is subsequently used to predict Tc for LM alloys, revealing In0.5 Sn0.5 as having the highest Tc at 7.01 K. Furthermore, we extended the prediction to 2,145 alloys binary and 45,670 ternary alloys across 66 metal elements and promising results were achieved. This work demonstrates the advantages of tree-based models in predicting Tc and would help accelerate the discovery of high-performance LM alloy superconductors in the coming time.  \nKeywords: Liquid metal; Machine learning; Transition temperature; Superconductor  \n1. Introduction  \nSuperconductors exhibit many fascinating properties, including the transmission of high currents without the generation of heat due to zero resistance. These properties render them a promising candidate for advanced devices, including power transmission systems, quantum computers, and micromagnetic resonance devices. Nevertheless, the extensive utilization of superconductors has met two considerable obstacles. (1) The conduction currents of a superconductor are zero-resistance exclusively below the Tc. It is typical for superconductors to be cooled to temperatures approaching or below the boiling point of nitrogen (77 K) in order to exhibit zero-resistance characteristics. (2) The theory for predicting Tc remains an open question that has puzzled the researchers since the discovery of superconductivity by Onnes in Leiden in 1911. This illustrates the significance ofTc as a crucial parameter in superconductor applications, as it defines  \nthe operational limits. The Tc of different superconductors varies according to their chemical composition.  \nInterestingly, the first ever superconductor was discovered in mercury (Hg), a typeof liquid metal (LM, metals with a melting point at or near room temperature), but its environmental toxicity has restricted the practical utility. Subsequently, study on superconductors has predominantly shifted to other superconducting materials, such as copper base superconductors, iron base superconductors, and hydrides etc. For many decades, there is a paucity of study pertaining to the superconducting properties of LM alloys. With the recent emergence of room temperature LM science and technology, many unknown properties of LM are being increasingly discovered, among which the superconductivity shown by Ga-based alloys has especially attracted attention from researchers 1. Ga-based alloys represent a type of low-melting-point metals that exhibit a number of advantageous characteristics, including room-temperature fluidity, nontoxicity, ease of manufacturing, rapid patterning2 and self-healing ability3. The fabrication of such superconducting films and circuits is a relatively straightforward and rapid process, rendering it an appropriate material in making superconducting flexible devices. As it was noticed, the con","cbCaioJtik371dCo","https://ap.wps.com/l/cbCaioJtik371dCo","pdf",2160196,18,"English","# Abstract\n# 1. Introduction\n## Superconductors and the role of Tc\n## Liquid metal alloys and research motivation\n## Limits of existing prediction theories","[{\"question\":\"What problem does the paper address about superconductors?\",\"answer\":\"Accurate prediction of superconductors’ critical temperature (Tc) remains difficult, and superconductors are also constrained by low Tc, limiting practical advanced applications.\"},{\"question\":\"How was the machine learning model built and evaluated?\",\"answer\":\"Using the SuperCon dataset, the authors preprocess the data, compare multiple machine learning models, and evaluate performance with R² and RMSE, finding Extra Trees performs best.\"},{\"question\":\"What results are reported for liquid metal alloy superconductors?\",\"answer\":\"The Extra Trees model is used to predict Tc for liquid metal (LM) alloys, identifying In0.5Sn0.5 as having the highest Tc at 7.01 K, and extending predictions across 2,145 binary and 45,670 ternary alloys with promising outcomes.\"}]","Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor - Abstract and Introduction | PDF",45]