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.