Benchmark for Ab Initio Prediction of Magnetic Structures based on Cluster-Multipole Theory

TL;DR

This paper introduces a benchmark for predicting magnetic structures using cluster-multipole expansion combined with spin-density functional theory, demonstrating high accuracy and efficiency in reproducing experimental magnetic configurations.

Contribution

It develops a systematic CMP-based approach integrated with SDFT for ab initio prediction of magnetic structures, validated by extensive benchmarking.

Findings

CMP expansion provides an exhaustive list of candidate magnetic structures.

CMP+SDFT narrows down magnetic configurations effectively.

SDFT reproduces experimental magnetic configurations within ±0.5 μB.

Abstract

The cluster multipole (CMP) expansion for magnetic structures provides a scheme to systematically generate candidate magnetic structures specifically including noncollinear magnetic configurations adapted to the crystal symmetry of a given material. A comparison with the experimental data collected on MAGNDATA shows that the most stable magnetic configurations in nature are linear combinations of only few CMPs. Furthermore, a high-throughput calculation for all candidate magnetic structures is performed in the framework of spin-density functional theory (SDFT). We benchmark the predictive power of CMP+SDFT with $2935$ calculations, which show that (i) the CMP expansion administers an exhaustive list of candidate magnetic structures, (ii) CMP+SDFT can narrow down the possible magnetic configurations to a handful of computed configurations, and (iii) SDFT reproduces the experimental magnetic configurations with an accuracy of $\pm0.5\,\mu_\text{B}$. For a subset the impact of on-site Coulomb repulsion $U$ is investigated by means of $1545$ CMP+SDFT+U calculations revealing no further improvement on the predictive power.