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http://hdl.handle.net/10362/155114| Title: | Roadmap on artificial intelligence and big data techniques for superconductivity |
| Author: | Yazdani-Asrami, Mohammad Song, Wenjuan Morandi, Antonio de Carne, Giovanni Murta-Pina, João Pronto, Anabela Oliveira, Roberto Grilli, Francesco Pardo, Enric Parizh, Michael Shen, Boyang Coombs, Tim Salmi, Tiina Wu, Di Coatanea, Eric Moseley, Dominic A. Badcock, Rodney A. Zhang, Mengjie Marinozzi, Vittorio Tran, Nhan Wielgosz, Maciej Skoczeń, Andrzej Tzelepis, Dimitrios Meliopoulos, Sakis Vilhena, Nuno Sotelo, Guilherme Jiang, Zhenan Große, Veit Bagni, Tommaso Mauro, Diego Senatore, Carmine Mankevich, Alexey Amelichev, Vadim Samoilenkov, Sergey Yoon, Tiem Leong Wang, Yao Camata, Renato P. Chen, Cheng Chien Madureira, Ana Maria Abraham, Ajith |
| Keywords: | applied superconductivity artificial intelligence big data deep learning machine learning neural network Ceramics and Composites Condensed Matter Physics Metals and Alloys Electrical and Electronic Engineering Materials Chemistry |
| Issue Date: | Apr-2023 |
| Citation: | 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 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 |
| 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. |
| Description: | 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. |
| Peer review: | yes |
| URI: | http://hdl.handle.net/10362/155114 |
| DOI: | https://doi.org/10.1088/1361-6668/acbb34 |
| ISSN: | 0953-2048 |
| Appears in Collections: | Home collection (FCT) |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Roadmap_on_artificial_intelligence_and_big_data.pdf | 6,77 MB | Adobe PDF | View/Open |
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