Journal of the Chinese Ceramic Society, Volume. 51, Issue 2, 411(2023)

Discovering High-Temperature Conventional Superconductors via Machine Learning

CUI Zhiqiang1,*... LUO Ying1, and ZHANG Yunwei12 |Show fewer author(s)
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • show less

    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)

    Tools

    Get Citation

    Copy Citation Text

    CUI Zhiqiang, LUO Ying, ZHANG Yunwei. Discovering High-Temperature Conventional Superconductors via Machine Learning[J]. Journal of the Chinese Ceramic Society, 2023, 51(2): 411

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Special Issue:

    Received: Nov. 27, 2022

    Accepted: --

    Published Online: Mar. 11, 2023

    The Author Email: Zhiqiang CUI (cuizhq3@mail2.sysu.edu.cn)

    DOI:10.14062/j.issn.0454-5648.20221022

    Topics