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    Combining quasiparticle self-consistent GW and machine-learned DFT+U to assess half-metallicity in Co- and Ni-based Heuslers

    Zefeng Cai1, Malcolm J. A. Jardine1, Maituo Yu1, Chenbo Min1, Jiatian Wu1, Hantian Liu1, Derek Dardzinski1, Christopher J. Palmstrøm2,3, and Noa Marom1,4,5,*

    • *Contact author: nmarom@andrew.cmu.edu

    Phys. Rev. Materials 10, 094411 – Published 24 September, 2026

    DOI: https://doi.org/10.1103/6y6t-xkx4

    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 semilocal and hybrid functionals are compared with quasiparticle self-consistent GW (QPGW). We also assess DFT with machine-learned Hubbard U corrections, as proposed in M. Yu et al. [npj Comput. Mater. 6, 180 (2020)]. Here, the U values that yield the closest agreement with the QPGW band structure are determined using an updated Bayesian optimization (BO) objective function that considers the atomic magnetic moments in addition to the band structure. 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, Co2TiSn and Co2ZrAl are the most likely to be half-metals.

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