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Discovering High-Temperature Conventional Superconductors via Machine Learning
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Zhiqiang CUI1, Ying LUO1, Yunwei ZHANG1, 2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 411 - 415
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Journal of the Chinese Ceramic Society | 2023, 51(2): 411-415
• Research Articles •
Discovering High-Temperature Conventional Superconductors via Machine Learning
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Zhiqiang CUI1, Ying LUO1, Yunwei ZHANG1, 2
Affiliations
  • 1.School of Physics, Sun Yat-sen University, Guangzhou 510275, China
  • 2.Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, China

    • CUI Zhiqiang (1977-), male, Master candidate. E-mail:

About Author:

CUI Zhiqiang (1977-), male, Master candidate. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20221022
Outline
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Searching for high-temperature ambient-pressure superconductors is a challenge in materials science. Machine learning has a promising application in materials discovery. A data-driven approach that overcomes low-data limitations by computationally inexpensive descriptors based on the Bardeen-Cooper-Schrieffer (BCS) theory and semi-supervised learning was proposed. The accuracy of the classification mode is 72%. This approach can screen over 10 000 binary and ternary BCS compounds in the Material Project database, thus identifying some promising superconductors at ambient pressure. The compounds in B-C and B-C-N systems have a maximum superconducting critical temperature (TC) of 60 K, which is greater than that for MgB2 (i.e., TC=39 K)

high-temperature superconductors  /  semi-supervised learning  /  materials design  /  neural networks
崔志强, 罗颖, 张云蔚. 通过机器学习设计新型超导材料. 硅酸盐学报, 2023 , 51 (2) : 411 -415 . DOI: 10.14062/j.issn.0454-5648.20221022
Zhiqiang CUI, Ying LUO, Yunwei ZHANG. Discovering High-Temperature Conventional Superconductors via Machine Learning[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 411 -415 . DOI: 10.14062/j.issn.0454-5648.20221022
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Year 2023 volume 51 Issue 2
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Article Info
doi: 10.14062/j.issn.0454-5648.20221022
  • Receive Date：2022-11-27
  • Online Date：2026-10-20
  • Published：2023-02-20
Article Data
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History
  • Received：2022-11-27
  • Revised：2022-12-25
Funding
Affiliations
    1.School of Physics, Sun Yat-sen University, Guangzhou 510275, China
    2.Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou 510275, China

Corresponding:

ZHANG Yunwei (1989-), female, Ph.D., Associate Professor. E-mail:
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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