AI-accelerated metallized σ-bonding screening for superconductor discovery

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized σ-bonding picture, we introduce the σ-bonding density of states (σDOS) as an efficient physical descriptor to identify high-transition-temperature (Tc) superconductors from DFT-level electronic structure without explicit DFPT calculations. The evaluation of σDOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B13Se as an ambient-pressure superconductor candidate with predicted Tc > 40 K, together with a family of high-Tc B13X candidates, supporting the effectiveness of this discovery strategy.