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. , , # Accelerated discovery of topological metals for nanoscale interconnects Dataset DOI: [10.5061/dryad.12jm63zb7](10.5061/dryad.12jm63zb7) ## Description of the data and file structure ### Files and variables #### File: NanowireData.xlsx **Description:** Data table containing extract surface transmission as a function of geometry and doping. Computed surface energies are also included. Used to train regression model. ##### Variables * Compound + Transmission Direction/Surface: Chemical formula and geometry of nanowire for computations * Surface energy: Computed surface energy in eV/Å^2^ * Ef: Doping of Fermi energy in eV * Normalized Surface Transmission: Extracted value of surface transmission in units of (e^2^/h)/H where H is the height of the nanowire as detailed in Fig. 1. of the accompanying manuscript. #### File: Interconnect_Results_Summary.html **Description:** Interactive scatter plot of data in NanowireData.csv points are colored by the metric detailed in Fig. 5 of t...,