Roadmap on artificial intelligence and big data techniques for superconductivity

Mohammad Yazdani-Asrami, Wenjuan Song, Antonio Morandi, Giovanni De Carne, Joao 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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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 languageEnglish
Article number043501
JournalSuperconductor Science and Technology
Issue number4
Publication statusPublished - Apr 2023
Publication typeA1 Journal article-refereed


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

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Ceramics and Composites
  • Condensed Matter Physics
  • Metals and Alloys
  • Materials Chemistry
  • Electrical and Electronic Engineering


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