Accelerated discovery of topological metals for nanoscale interconnects

The sharp increase in resistivity of copper interconnects at ultra-scaled dimensions threatens the continued miniaturization of integrated circuits. Topological metals with gapless surface states (Fermi arcs) protected by bulk topological invariants offer robust, backscattering-immune conduction. We develop an efficient computational framework to quantify 0~K surface-state transmission in TSM nanowires derived from Wannier tight-binding models that faithfully reproduce relativistic density functional theory results. Utilizing the non-equilibrium Green's function formalism, we systematically screen materials across chemical potentials and transport directions, producing a dataset of 3000 surface transmission values. This dataset supports machine learning models for rapid interconnect compound identification.