Vacancy in graphene: insight on magnetic properties from theoretical modeling

Abstract

Magnetic properties of a single vacancy in graphene is a relevant and still unsolved problem. The experimental results point to a clearly detectable magnetic defect state at the Fermi energy, while several calculations based on density functional theory (DFT) yield widely varying results for the magnetic moment, in the range of $\mu =1.04-2.0$ ${\mu }_B$. We present a multi-tool \textit{ab initio} theoretical study of the same defect, using two simulation protocols for a defect in a crystal (cluster and periodic boundary conditions) and different DFT functionals - bare and hybrid DFT, mixing a fraction of exact Hartree-Fock exchange (XC). Our main conclusions are two-fold: First, we find that due to the $\pi$-character of the Fermi-energy states of graphene, inclusion of XC is crucial and for a single isolated vacancy we can predict an integer magnetic moment $\mu =2{\mu }_B$. Second, we find that due to the specific symmetry of the graphene lattice, periodic arrays of single vacancies may provide interesting diffuse spin-spin interactions.

arXiv admin note: substantial text overlap with arXiv:1611.08246

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• Vacancies in Graphene : Dirac Physics and Fractional Vacuum Charges
• Point defect formation energies in graphene from diffusion quantum Monte Carlo and density functional theory
• Breakdown of topological protection due to non-magnetic edge disorder in two-dimensional materials in the Quantum Spin Hall phase
• Structural, electronic and magnetic properties of point defects in polyaniline (C3N) and graphene monolayers: A comparative study
• Kondo effect due to a hydrogen impurity in graphene: a multichannel Kondo problem with diverging hybridization
• Fluorine Intercalated Graphene: Formation of a 2D Spin Lattice through Pseudoatomization
• Collective resonances near zero energy induced by a point defect in bilayer graphene
• Local multiplet formation around a single vacancy in graphene: an effective Anderson model analysis based on the block-Lanczos DMRG method