Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers

TL;DR

This study compares different computational methods to accurately predict the electronic and magnetic properties of lattice-matched Heusler compounds for spintronics applications, highlighting the effectiveness of machine-learned DFT+U.

Contribution

It introduces a Bayesian optimization approach to determine Hubbard U parameters in DFT+U, aligning it closely with quasiparticle self-consistent GW results for Heusler compounds.

Findings

DFT+U(BO) reproduces key GW features in most cases.

Method dependence affects spin polarization and dominant spin channel.

Co2TiSn and Co2ZrAl are promising half-metal candidates.

Abstract

Half-metallic Heusler compounds are of significant interest for spintronics. For device fabrication, compounds that can be epitaxially grown on III-V semiconductors are particularly attractive. We present a first-principles investigation of four Co-based and two Ni-based Heusler compounds that are lattice-matched to InAs. The results of density functional theory (DFT) using semi-local and hybrid functionals are compared to quasiparticle self-consistent $GW$ (QPGW). We also consider DFT with machine-learned Hubbard $U$ corrections [npj Computational Materials 6, 180 (2020)] with a new Bayesian optimization (BO) objective function to determine the $U$ values that yield the closest agreement with the QPGW band structure and magnetic moments. We find that DFT+U(BO) can adequately reproduce the key QPGW features in most cases. Our results reveal a strong method dependence of the degree of spin polarization at the Fermi level and, in some cases, even the dominant spin channel (majority or minority). Of the materials studied here, Co$_2$TiSn and Co$_2$ZrAl are the most likely to be half-metals, and Co$_2$MnIn is likely to be a near-half-metal.