@article{9ee347056077412cb0b894fdb95fbdd6,
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 Joao 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 = "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: Supported by Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, Portugal, with reference UIDB/00066/2020. 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",
}