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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

DOI 10.1103/PhysRevB.108.024512 · Physical Review B

T1

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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 layered borocarbides (stable CaBC5 and metastable Ca2BC11 and CaB3C3) are predicted to be phonon-mediated superconductors at ambient pressure. The stable CaBC5 and the low-energy metastable Ca2BC11 (with formation energy only 9.5 meV/atom above the convex hull) have a superconducting Tc of 5.2 and 8.9 K, respectively. While the hexagonal CaB3C3 possesses a Tc of 26.1 K, it is metastable with formation energy of 153 meV/atom above the convex hull. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-Tc superconductors.

Source-reported materials — not catalogue approval

FormulaReported Tc (K)Pressure (GPa)Type
CaBC5

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5.2Pressure unresolvedunknown
Ca2BC11

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8.9Pressure unresolvedunknown
CaB3C3

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26.1Pressure unresolvedunknown
MgB2

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39Pressure not reportedunknown
CaC6

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11.5Pressure not reportedunknown
SrB3C3

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40Pressure not reportedunknown
KPbB6C6

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

88Pressure unresolvedunknown

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