High-throughput Search for Metallic Altermagnets by Embedded Dynamical Mean Field Theory

TL;DR

This paper presents a high-throughput computational method combining DFT and eDMFT to efficiently discover metallic altermagnets, identifying new candidates and analyzing their prevalence among magnetic materials.

Contribution

It introduces an automated workflow integrating symmetry analysis with advanced electronic structure calculations to accelerate altermagnet discovery.

Findings

Identified two new metallic altermagnets, CrSe and CaFe4Al8.

Discovered a dozen semiconducting altermagnets.

Found that metallic altermagnets are rare among magnetic materials.

Abstract

Altermagnets (AM) are a novel class of magnetic materials with zero net magnetization but broken time-reversal symmetry and spin-split bands exceeding the spin-orbit coupling scale, offering unique control of individual spin-channel and high charge-spin conversion efficiency for spintronic applications. Still, only a few metallic altermagnets have been identified, and discovering them through trial-and-error is resource-intensive. Here, we introduce a high-throughput screening strategy to accelerate the discovery of materials with altermagnetic properties. By combining density functional theory (DFT) with embedded dynamical mean-field theory (eDMFT), our approach improves the accuracy in predicting metallicity and spin splitting, especially in transition-metal-rich compounds. An automated workflow incorporates pre-screening and symmetry analysis to reduce both human effort and computational cost. This approach identified two previously unreported metallic altermagnets, CrSe and CaFe4Al8 (in addition to two known altermagnets, CrSb and RuO2), as well as a dozen semiconducting altermagnets among over 2,000 magnetic materials. Our findings reveal that while altermagnets are abundant among magnetic materials, only a tiny fraction is metallic.