Machine learning guided discovery of superconducting calcium borocarbides

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 T-c 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.