Roadmap on artificial intelligence and big data techniques for superconductivity

Mohammad Yazdani-Asrami; Wenjuan Song; Antonio Morandi; Giovanni de Carne; João Murta-Pina; Anabela Pronto; Roberto Oliveira; Francesco Grilli; Enric Pardo; Michael Parizh; Boyang Shen; Tim Coombs; Tiina Salmi; Di Wu; Eric Coatanea; Dominic A. Moseley; Rodney A. Badcock; Mengjie Zhang; Vittorio Marinozzi; Nhan Tran; Maciej Wielgosz; Andrzej Skoczeń; Dimitrios Tzelepis; Sakis Meliopoulos; Nuno Vilhena; Guilherme Sotelo; Zhenan Jiang; Veit Große; Tommaso Bagni; Diego Mauro; Carmine Senatore; Alexey Mankevich; Vadim Amelichev; Sergey Samoilenkov; Tiem Leong Yoon; Yao Wang; Renato P. Camata; Cheng Chien Chen; Ana Maria Madureira; Ajith Abraham

doi:10.1088/1361-6668/acbb34

Roadmap on artificial intelligence and big data techniques for superconductivity

Mohammad Yazdani-Asrami, Wenjuan Song, Antonio Morandi, Giovanni de Carne, João Murta-Pina, Anabela Pronto, Roberto Oliveira, Francesco Grilli, Enric Pardo, Michael Parizh, Boyang Shen, Tim Coombs, Tiina Salmi, Di Wu, Eric Coatanea, Dominic A. Moseley, Rodney A. Badcock, Mengjie Zhang, Vittorio Marinozzi, Nhan TranMaciej Wielgosz, Andrzej Skoczeń, Dimitrios Tzelepis, Sakis Meliopoulos, Nuno Vilhena, Guilherme Sotelo, Zhenan Jiang, Veit Große, Tommaso Bagni, Diego Mauro, Carmine Senatore, Alexey Mankevich, Vadim Amelichev, Sergey Samoilenkov, Tiem Leong Yoon, Yao Wang, Renato P. Camata, Cheng Chien Chen, Ana Maria Madureira, Ajith Abraham

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Abstract

This 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.

Original language	English
Article number	043501
Number of pages	58
Journal	Superconductor Science and Technology
Volume	36
Issue number	4
DOIs	https://doi.org/10.1088/1361-6668/acbb34
Publication status	Published - Apr 2023

Keywords

applied superconductivity
artificial intelligence
big data
deep learning
machine learning
neural network

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10.1088/1361-6668/acbb34Licence: CC BY

Roadmap on artificial intelligence and big dataFinal published version, 6.61 MBLicence: CC BY

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Yazdani-Asrami, M., Song, W., Morandi, A., de Carne, G., Murta-Pina, J., Pronto, A., Oliveira, R., Grilli, F., Pardo, E., Parizh, M., Shen, B., Coombs, T., Salmi, T., Wu, D., Coatanea, E., Moseley, D. A., Badcock, R. A., Zhang, M., Marinozzi, V., ... Abraham, A. (2023). Roadmap on artificial intelligence and big data techniques for superconductivity. Superconductor Science and Technology, 36(4), Article 043501. https://doi.org/10.1088/1361-6668/acbb34

@article{8eac704e65bf4aea9b952d5d192f3937,

title = "Roadmap on artificial intelligence and big data techniques for superconductivity",

abstract = "This 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.",

keywords = "applied superconductivity, artificial intelligence, big data, deep learning, machine learning, neural network",

author = "Mohammad Yazdani-Asrami and Wenjuan Song and Antonio Morandi and {de Carne}, Giovanni and Jo{\~a}o Murta-Pina and Anabela Pronto and Roberto Oliveira and Francesco Grilli and Enric Pardo and Michael Parizh and Boyang Shen and Tim Coombs and Tiina Salmi and Di Wu and Eric Coatanea and Moseley, {Dominic A.} and Badcock, {Rodney A.} and Mengjie Zhang and Vittorio Marinozzi and Nhan Tran and Maciej Wielgosz and Andrzej Skocze{\'n} and Dimitrios Tzelepis and Sakis Meliopoulos and Nuno Vilhena and Guilherme Sotelo and Zhenan Jiang and Veit Gro{\ss}e and Tommaso Bagni and Diego Mauro and Carmine Senatore and Alexey Mankevich and Vadim Amelichev and Sergey Samoilenkov and Yoon, {Tiem Leong} and Yao Wang and Camata, {Renato P.} and Chen, {Cheng Chien} and Madureira, {Ana Maria} and Ajith Abraham",

note = "info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT# Funding 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 {\textquoteleft}{\textquoteleft}Advanced Energy Technology Platforms{\textquoteright}{\textquoteright} 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: {\textcopyright} 2023 The Author(s). Published by IOP Publishing Ltd.",

year = "2023",

month = apr,

doi = "10.1088/1361-6668/acbb34",

language = "English",

volume = "36",

journal = "Superconductor Science and Technology",

issn = "0953-2048",

publisher = "IOP Publishing",

number = "4",

}

Yazdani-Asrami, M, Song, W, Morandi, A, de Carne, G, Murta-Pina, J , Pronto, A, Oliveira, R, Grilli, F, Pardo, E, Parizh, M, Shen, B, Coombs, T, Salmi, T, Wu, D, Coatanea, E, Moseley, DA, Badcock, RA, Zhang, M, Marinozzi, V, Tran, N, Wielgosz, M, Skoczeń, A, Tzelepis, D, Meliopoulos, S, Vilhena, N, Sotelo, G, Jiang, Z, Große, V, Bagni, T, Mauro, D, Senatore, C, Mankevich, A, Amelichev, V, Samoilenkov, S, Yoon, TL, Wang, Y, Camata, RP, Chen, CC, Madureira, AM & Abraham, A 2023, 'Roadmap on artificial intelligence and big data techniques for superconductivity', Superconductor Science and Technology, vol. 36, no. 4, 043501. https://doi.org/10.1088/1361-6668/acbb34

TY - JOUR

T1 - Roadmap on artificial intelligence and big data techniques for superconductivity

AU - Yazdani-Asrami, Mohammad

AU - Song, Wenjuan

AU - Morandi, Antonio

AU - de Carne, Giovanni

AU - Murta-Pina, João

AU - Pronto, Anabela

AU - Oliveira, Roberto

AU - Grilli, Francesco

AU - Pardo, Enric

AU - Parizh, Michael

AU - Shen, Boyang

AU - Coombs, Tim

AU - Salmi, Tiina

AU - Wu, Di

AU - Coatanea, Eric

AU - Moseley, Dominic A.

AU - Badcock, Rodney A.

AU - Zhang, Mengjie

AU - Marinozzi, Vittorio

AU - Tran, Nhan

AU - Wielgosz, Maciej

AU - Skoczeń, Andrzej

AU - Tzelepis, Dimitrios

AU - Meliopoulos, Sakis

AU - Vilhena, Nuno

AU - Sotelo, Guilherme

AU - Jiang, Zhenan

AU - Große, Veit

AU - Bagni, Tommaso

AU - Mauro, Diego

AU - Senatore, Carmine

AU - Mankevich, Alexey

AU - Amelichev, Vadim

AU - Samoilenkov, Sergey

AU - Yoon, Tiem Leong

AU - Wang, Yao

AU - Camata, Renato P.

AU - Chen, Cheng Chien

AU - Madureira, Ana Maria

AU - Abraham, Ajith

N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT# Funding 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.

PY - 2023/4

Y1 - 2023/4

N2 - This 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.

AB - This 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.

KW - applied superconductivity

KW - artificial intelligence

KW - big data

KW - deep learning

KW - machine learning

KW - neural network

UR - http://www.scopus.com/inward/record.url?scp=85149144047&partnerID=8YFLogxK

U2 - 10.1088/1361-6668/acbb34

DO - 10.1088/1361-6668/acbb34

M3 - Article

AN - SCOPUS:85149144047

SN - 0953-2048

VL - 36

JO - Superconductor Science and Technology

JF - Superconductor Science and Technology

IS - 4

M1 - 043501

ER -

Roadmap on artificial intelligence and big data techniques for superconductivity

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