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arXiv · 2303.09342

Machine learning guided discovery of superconducting calcium borocarbides

Chao Zhang·Hui Tang·Chen Pan·Hong Jiang·Huai-Jun Sun·Kai-Ming Ho·Cai-Zhuang Wang
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Abstract

Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds. Three new layered borocarbides (stable CaBC5 and metastable Ca2BC11 and CaB3C3) are predicted to be phonon-mediated superconductors at ambient pressure. The hexagonal CaB3C3 possesses the highest Tc of 26.05 K among the three compounds. The {\sigma}-bonging bands around the Fermi level account for the large electron-phonon coupling ({\lambda} = 0.980) of hexagonal CaB3C3. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-Tc superconductors.

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Chao Zhang, Hui Tang, Chen Pan, Hong Jiang, Huai-Jun Sun, Kai-Ming Ho, Cai-Zhuang Wang. 2023-03-16. Machine learning guided discovery of superconducting calcium borocarbides. https://doi.org/10.1103/physrevb.108.024512

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