Journal of the Chinese Ceramic Society, Volume. 51, Issue 2, 411(2023)
Discovering High-Temperature Conventional Superconductors via Machine Learning
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)
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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
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Received: Nov. 27, 2022
Accepted: --
Published Online: Mar. 11, 2023
The Author Email: Zhiqiang CUI (cuizhq3@mail2.sysu.edu.cn)