[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128784-en":3,"doc-seo-128784-105":31,"detail-sidebar-cat-0-en-105":84},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128784,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiation - first machine learning study","This paper introduces an intelligent estimator for the critical current of high-temperature superconductor (HTS) tapes subjected to gamma or neutron radiation, leveraging machine learning (ML). A benchmarking study compares ten ML methods to identify the most effective models for each radiation type. To support generalisation, experimental datasets are assembled through a review of 90 published papers, covering multiple ReBCO tape variants. Results show cascade-forward neural networks provide the strongest prediction accuracy for both cases, with markedly high goodness of fit and low relative errors.","Superconductor Science and  \nTechnology   \nLETTER • OPEN ACCESS  \nIntelligent estimation of critical current density of ReBCO superconductors exposed to irradiation: first machine learning study  \nTo cite this article: Shahin Alipour Bonab et al 2025 Supercond. Sci. Technol. 38 09LT01  \nView the article online for updates and enhancements.  \nYou may also like  \n-Probing superconducting ground states in VEC-optimized Hf–Ta–Nb–Mo–W high entropy alloys  \nManikandan Krishnan, Jiaojiao Meng, Cao Wang et al.  \n-Effect of Gamma Irradiation on DC Performance of Circular-Shaped AlGaN/GaN High Electron Mobility Transistors  \nYa-Hsi Hwang, Yueh-Ling Hsieh, L Lei et al.  \n-Numerical simulation on the threshold field and AC loss in vertical stacks of REBCO tapes carrying DC transport currents under perpendicular AC magnetic fields  \nShun Miura, Yueming Sun, Hiromasa Sasa et al.  \nThis content was downloaded from IP address [130.209.108.254](130.209.108.254) on 17/11/2025 at 13:24  \nSuperconductor Science and Technology  \nSupercond. Sci. Technol. 38 (2025) 09LT01 (15pp) [https://doi.org/10.1088/1361-6668/ae011e](https://doi.org/10.1088/1361-6668/ae011e)  \nLetter  \nIntelligent estimation of critical current density of ReBCO superconductors exposed to irradiation: first machine learning study  \nShahin Alipour Bonab􀁂, Yahao Wu􀁂, Wenjuan Song􀁂 and Mohammad Yazdani-Asrami∗ 􀁂  \nCryoElectric Research Lab, Propulsion, Electrification & Superconductivity Group, Autonomous Systems and Connectivity Division, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, United Kingdom  \n[E-mail:](E-mail: mohammad.yazdani-asrami@glasgow.ac.uk)[ mohammad.yazdani-asrami@glasgow.ac.uk](E-mail: mohammad.yazdani-asrami@glasgow.ac.uk)  \nReceived 26 May 2025, revised 22 August 2025 Accepted for publication 31 August 2025  \nPublished 23 September 2025  \nAbstract  \nThis paper presents the first intelligent estimator model of the critical current of  \nhigh-temperature superconductor (HTS) tapes exposed to gamma or neutron radiation using machine learning (ML) techniques. A comprehensive benchmarking analysis of ten ML methods has been conducted to determine the best ML models for each type of radiation. To ensure the generalisability of the models, databases of experimental measurements were collected by an extensive review of 90 published papers in the literature, covering four and nine different rare-earth barium copper oxide (ReBCO) tapes for gamma and neutron irradiation tests, respectively. The results demonstrated that the cascade-forward neural network (CFNN) excels for both gamma and neutron irradiation prediction models. For the gamma irradiation model, the CFNN model’s performance in terms of goodness of fit and relative error was 99.979% and 0.2675%, respectively. For the neutron irradiation model, these metrics have shown a performance of 99.972% and 4.68% . The findings of this paper will advance the modelling of superconductors in terms of understanding their behaviour after irradiation for fusion applications.  \nKeywords: artificial intelligence, gamma ray, HTS tape, neutron irradiation, nuclear fusion, radiation effect, critical current prediction  \n∗  \nAuthor to whom any correspondence should be addressed.  \nOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any  \nfurther distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \n1 © 2025 The Author(s) . Published by IOP Publishing Ltd  \n1. Introduction  \nBeing responsible for 40% of the global CO2 emissions, the energy sector is currently a major pollution contributor worldwide. Different countries have already introduced programmes to curb CO2 , such as Net Zero 50 in the UK. High-temperature superconductors (HTSs) can significantly contribute to international efforts to decarbonise the energy sector by offering one of the most promising options for designing de","cbCaif9zohORGCGP","https://ap.wps.com/l/cbCaif9zohORGCGP","pdf",2741086,2,1,16,"English","en",105,"# Abstract\n# Introduction\n## Motivation from energy decarbonisation and fusion needs\n## Radiation exposure in fusion reactors\n## Radiation-induced degradation mechanisms in ReBCO","[{\"question\":\"Which ML model shows the best performance?\",\"answer\":\"The cascade-forward neural network (CFNN) achieves the best results for both gamma and neutron irradiation prediction models.\"}]","Intelligent estimation of critical current density of ReBCO superconductors exposed to irradiation - first machine learning study | PDF",1786003415,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":29},"intelligent-estimation-of-critical-current-density-of-rebco-superconductors-exposed-to-irradiation-first-machine-learning-study","",{"@graph":37,"@context":78},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/intelligent-estimation-of-critical-current-density-of-rebco-superconductors-exposed-to-irradiation-first-machine-learning-study/128784/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"Which ML model shows the best performance?","Question",{"text":76,"@type":77},"The cascade-forward neural network (CFNN) achieves the best results for both gamma and neutron irradiation prediction models.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":85},[86,90,94,98,103,108,112,115,120,123,127],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":30,"slug":111},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":99,"slug":130},19,"General","general"]