Discovering High-Temperature Conventional Superconductors via Machine Learning

被引：0
作者：
Cui Z. [1 ]
Luo Y. [1 ]
Zhang Y. [1 ,2 ]
机构：
[1] School of Physics, Sun Yat-sen University, Guangzhou
[2] Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, Sun Yat-sen University, Guangzhou
来源：
Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society | 2023年 / 51卷 / 02期
关键词：
high-temperature superconductors; materials design; neural networks; semi-supervised learning;
D O I：
10.14062/j.issn.0454-5648.20221022
中图分类号：
学科分类号：
摘要：
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). © 2023 Chinese Ceramic Society. All rights reserved.
引用
收藏
页码：411 / 415
页数：4
相关论文
共 38 条
  • [1] XIAO L, LIN L., Recent progress of power application of superconductor in China[J], IEEE Transact Appl Supercond, 17, 2, pp. 2355-2360, (2017)
  • [2] KRABBES G, FUCHS G, CANDERS W R, Et al., High temperature superconductor bulk materials: Fundamentals-Processing-Properties Control-Application Aspects, (2006)
  • [3] BALATSKY, ALEXANDER V, ILYA VEKHTER, Et al., Impurity-induced states in conventional and unconventional superconductors, Rev Modern Phys, 78, 2, (2006)
  • [4] LILIA B, HENNIG R, HIRSCHFELD P, Et al., The 2021 room-temperature superconductivity roadmap, J Phys: Condensed Matter, 34, (2021)
  • [5] COOPER Leon N., Bound electron pairs in a degenerate Fermi gas, Phys Rev, 104, 4, (1956)
  • [6] WANG Yanchao, LV Jian, ZHU Li, Et al., CALYPSO: A method for crystal structure prediction[J], Computer Phys Commun, 183, 10, pp. 2063-2070, (2012)
  • [7] GLASS C W, OGANOV A R, HANSEN N., USPEX—Evolutionary crystal structure prediction[J], Computer Phys Commun, 175, 11, pp. 713-720, (2006)
  • [8] PICKARD C J, NEEDS R J., Ab initio random structure searching, J Phys: Condensed Matter, 23, 5, (2011)
  • [9] LI Y, HAO J, LIU H, Et al., The metallization and superconductivity of dense hydrogen sulfide, J Chem Phys, 140, 17, (2014)
  • [10] DROZDOV A P, EREMETS M I, TROYAN I A, Et al., Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system[J], Nature, 525, 7567, pp. 73-76, (2015)
1234