Artificial intelligence methods for applied superconductivity: material, design, manufacturing, testing, operation, and condition monitoring

Mohammad Yazdani-Asrami, Alireza Sadeghi, Wenjuan Song, Ana Madureira, João Murta-Pina, Antonio Morandi, Michael Parizh

Research output: Contribution to journalReview articlepeer-review

20 Citations (Scopus)


More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, healthcare, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing-related issues. In the current century, massive advancements have been achieved in artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards the full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, the concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large-scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. This topical review is presented in three main categories: AI for large-scale superconducting applications, AI for superconducting materials, and AI for the physics of SCs. In addition, the challenges of applying AI techniques to the superconductivity and its applications are given. Finally, future trends on how to integrate AI techniques with superconductivity towards commercialization are discussed.

Original languageEnglish
Article number123001
Number of pages55
JournalSuperconductor Science and Technology
Issue number12
Publication statusPublished - Dec 2022


  • artificial intelligence
  • big data
  • deep learning
  • machine learning
  • optimisation
  • prediction
  • superconductors


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