Machine-learning-guided discovery of kagome superconductors YRu3B2 and LuRu3B2

We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu₃B₂ and LuRu₃B₂, which were predicted through machine-learning-accelerated high-throughput screening combined with first-principles calculations. These materials crystallize in the hexagonal CeCo₃B₂-type structure with planar kagome networks formed by Ru atoms. We observe superconducting critical temperatures of T_c=0.81 K for YRu₃B₂ and T_c=0.95 K for LuRu₃B₂, confirmed through magnetization, specific heat, and electrical transport measurements. Both compounds exhibit nearly 100% superconducting volume fractions, demonstrating bulk superconductivity. Compared with isostructural LaRu₃Si₂, YRu₃B₂ and LuRu₃B₂ show a more dispersive Ru local d_x2−y2 quasiflat band [and thus a reduced density of states (DOS) at EF] together with an overall hardening of the phonon spectrum, both of which lower the electron-phonon coupling (EPC) constant λ. Meanwhile, the dominant real-space EPC between Ru local d_x2−y2 states and the low-frequency Ru in-plane local x branch remains nearly unchanged, indicating that the reduction of λ originates from the d_x2−y2 DOS reduction and the overall phonon hardening. Superfluid weight calculations show that conventional contributions dominate over quantum geometric effects due to the dispersive nature of bands near the Fermi level. This work demonstrates the effectiveness of integrating machine-learning screening, first-principles theory, and experimental synthesis for accelerating the discovery of new superconducting materials.