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Roadmap on artificial intelligence and big data techniques for superconductivity

2023-04Artigo científico Acesso aberto
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dc.contributor.authorYazdani-Asrami, Mohammad
dc.contributor.authorSong, Wenjuan
dc.contributor.authorMorandi, Antonio
dc.contributor.authorde Carne, Giovanni
dc.contributor.authorMurta-Pina, João
dc.contributor.authorPronto, Anabela
dc.contributor.authorOliveira, Roberto
dc.contributor.authorGrilli, Francesco
dc.contributor.authorPardo, Enric
dc.contributor.authorParizh, Michael
dc.contributor.authorShen, Boyang
dc.contributor.authorCoombs, Tim
dc.contributor.authorSalmi, Tiina
dc.contributor.authorWu, Di
dc.contributor.authorCoatanea, Eric
dc.contributor.authorMoseley, Dominic A.
dc.contributor.authorBadcock, Rodney A.
dc.contributor.authorZhang, Mengjie
dc.contributor.authorMarinozzi, Vittorio
dc.contributor.authorTran, Nhan
dc.contributor.authorWielgosz, Maciej
dc.contributor.authorSkoczeń, Andrzej
dc.contributor.authorTzelepis, Dimitrios
dc.contributor.authorMeliopoulos, Sakis
dc.contributor.authorVilhena, Nuno
dc.contributor.authorSotelo, Guilherme
dc.contributor.authorJiang, Zhenan
dc.contributor.authorGroße, Veit
dc.contributor.authorBagni, Tommaso
dc.contributor.authorMauro, Diego
dc.contributor.authorSenatore, Carmine
dc.contributor.authorMankevich, Alexey
dc.contributor.authorAmelichev, Vadim
dc.contributor.authorSamoilenkov, Sergey
dc.contributor.authorYoon, Tiem Leong
dc.contributor.authorWang, Yao
dc.contributor.authorCamata, Renato P.
dc.contributor.authorChen, Cheng Chien
dc.contributor.authorMadureira, Ana Maria
dc.contributor.authorAbraham, Ajith
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.pblIOP Publishing
dc.date.accessioned2023-07-11T22:23:12Z
dc.date.available2023-07-11T22:23:12Z
dc.date.issued2023-04
dc.descriptionFunding Information: Financial support was provided by the Swiss National Science Foundation (Grant No. 200021_184940). Funding Information: Networking support provided by the European Cooperation in Science and Technology, COST Action CA19108 (Hi-SCALE) is acknowledged. Funding Information: A part of this work was supported by the Russian National Technology Initiative Foundation (Grant ID 0000000007418QR20002). Funding Information: The research was also supported by the European Synchrotron Radiation Facility (Grant No. MA-2767). Funding Information: This work was supported in part by the New Zealand Ministry of Business, Innovation and Employment (MBIE) by the Strategic Science Investment Fund ‘‘Advanced Energy Technology Platforms’’ under Contract RTVU2004. Funding Information: Y Wang acknowledges support from the National Science Foundation (NSF) Award DMR-2132338. R P Camata and C-C Chen are supported by the FTPP Program funded by NSF EPSCoR RII Track-1 Cooperative Agreement OIA-2148653. C-C Chen also acknowledges support from the NSF Award DMR-2142801. Publisher Copyright: © 2023 The Author(s). Published by IOP Publishing Ltd.
dc.description.abstractThis paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 yr time-frame.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent58
dc.format.extent6928504
dc.identifier.doi10.1088/1361-6668/acbb34
dc.identifier.issn0953-2048
dc.identifier.otherPURE: 65902268
dc.identifier.otherPURE UUID: 8eac704e-65bf-4aea-9b95-2d5d192f3937
dc.identifier.otherScopus: 85149144047
dc.identifier.otherWOS: 000939024000001
dc.identifier.otherORCID: /0000-0001-6305-0265/work/151369265
dc.identifier.urihttp://hdl.handle.net/10362/155114
dc.identifier.urlhttps://www.scopus.com/pages/publications/85149144047
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.relationCentre of Technology and Systems
dc.subjectapplied superconductivity
dc.subjectartificial intelligence
dc.subjectbig data
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectneural network
dc.subjectCeramics and Composites
dc.subjectCondensed Matter Physics
dc.subjectMetals and Alloys
dc.subjectElectrical and Electronic Engineering
dc.subjectMaterials Chemistry
dc.titleRoadmap on artificial intelligence and big data techniques for superconductivityen
dc.typejournal article
degois.publication.issue4
degois.publication.titleSuperconductor Science and Technology
degois.publication.volume36
dspace.entity.typePublication
oaire.awardNumberUIDB/00066/2020
oaire.awardTitleCentre of Technology and Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication779ae807-14a4-4e58-87e2-6dd81eb7a030
relation.isProjectOfPublication.latestForDiscovery779ae807-14a4-4e58-87e2-6dd81eb7a030

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