机器学习引导的超导钙硼碳化物发现
Machine learning guided discovery of superconducting calcium borocarbides
AI总结：
本研究结合机器学习与第一性原理计算，在Ca-B-C体系中预测出三种常压声子介导超导体，其中六方CaB3C3的Tc最高达26.05 K，为加速发现高Tc超导体提供了新途径。
摘要：
在常压下于轻元素体系中追求超导电性具有重要的实验和理论意义。本工作将机器学习方法与第一性原理计算相结合，高效搜索能量有利的三元Ca-B-C化合物。预测三种新型层状硼碳化物（稳定的CaBC5以及亚稳的Ca2BC11和CaB3C3）在常压下为声子介导的超导体。其中六方CaB3C3在三种化合物中具有最高的Tc，为26.05 K。费米能级附近的σ成键带解释了六方CaB3C3的大电子-声子耦合（λ = 0.980）。该机器学习引导的方法为大幅加速发现新型高Tc超导体开辟了途径。
英文摘要：
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 σ-bonging bands around the Fermi level account for the large electron-phonon coupling (λ = 0.980) of hexagonal CaB3C3. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-Tc superconductors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
