High-throughput study of electrical conductivity in ordered metals

TL;DR

This study combines machine learning and high-throughput ab initio calculations to identify and analyze new intermetallic compounds with high electrical conductivity, matching or surpassing known metals like aluminum.

Contribution

It introduces a computational framework that efficiently screens millions of compounds and validates predictions with electron-phonon calculations, revealing new high-conductivity materials.

Findings

Identified intermetallic candidates with conductivities comparable to aluminum.

Validated predictions with electron-phonon coupling calculations in agreement with experiments.

Demonstrated that light-element valence electrons can achieve high conductivity by shifting $d$-states below the Fermi level.

Abstract

We present a computational framework that integrates machine learning with high-throughput \textit{ab initio} calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum ($36.59 \times 10^6$~S/m). We perform full electron--phonon coupling calculations for the top-performing materials, yielding results in excellent agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) define the practical ceiling for conductivity due to their unique electronic structure and low scattering, compounds like $\text{LiBePt}_2$ can achieve comparable performance by utilizing valence electrons from light elements to shift high-scattering $d$-states beneath the Fermi level. This study not only identifies novel high-performance conductors but also demonstrates the predictive power of combining statistical learning with detailed ab initio calculations.