English

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

Materials Science 2026-02-25 v1 Strongly Correlated Electrons Computational Physics

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 GWGW (QPGW). We also consider DFT with machine-learned Hubbard UU corrections [npj Computational Materials 6, 180 (2020)] with a new Bayesian optimization (BO) objective function to determine the UU 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, Co2_2TiSn and Co2_2ZrAl are the most likely to be half-metals, and Co2_2MnIn is likely to be a near-half-metal.

Keywords

Cite

@article{arxiv.2602.20621,
  title  = {Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers},
  author = {Zefeng Cai and Malcolm J. A. Jardine and Maituo Yu and Chenbo Min and Jiatian Wu and Hantian Liu and Derek Dardzinski and Christopher J. Palmstrøm and Noa Marom},
  journal= {arXiv preprint arXiv:2602.20621},
  year   = {2026}
}