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BIBTEX RIS

Roadmap on artificial intelligence and big data techniques for superconductivity

2023; IOP Publishing; Volume: 36; Issue: 4 Linguagem: Inglês

10.1088/1361-6668/acbb34

ISSN

1361-6668

Autores

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, Éric Coatanéa, Dominic A. Moseley, Rodney A. Badcock, Mengjie Zhang, Vittorio Marinozzi, Nhan Viet Tran, Maciej Wielgosz, Andrzej Skoczeń, Dimitrios Tzelepis, A. P. Sakis Meliopoulos, Nuno Vilhena, Guilherme Gonçalves Sotelo, Zhenan Jiang, V. Große, Tommaso Bagni, Diego Mauro, Carmine Senatore, Alexey Mankevich, V. A. Amelichev, S. V. Samoilenkov, Tiem Leong Yoon, Yao Wang, Renato P. Camata, Cheng-Chien Chen, Ana Madureira, Ajith Abraham,

Tópico(s)

Machine Learning in Materials Science

Resumo

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.

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