Magnetic Compton profiles of Ni beyond the one-particle picture: Numerically exact and perturbative solvers of dynamical mean-field theory

Alyn D N James, M Sekania, Stephen B Dugdale, Liviu Chioncel

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Abstract

We calculated the magnetic Compton profiles (MCPs) of Ni using density functional theory supplemented by electronic correlations treated within dynamical mean-field theory (DMFT). We present comparisons between the theoretical and experimental MCPs. The theoretical MCPs were calculated using the Korringa-Kohn-Rostoker method with the perturbative spin-polarized T-matrix fluctuation exchange approximation DMFT solver, as well as with the full potential linear augmented plane-wave method with the numerically exact continuous-time quantum Monte Carlo DMFT solver. We show that the total magnetic moment decreases with the intra-atomic Coulomb repulsion U, which is also reflected in the corresponding MCPs. The total magnetic moment obtained in experimental measurements can be reproduced by intermediate values of U. The spectral function reveals that the minority X2 Fermi-surface pocket shrinks and gets shallower with respect to the density functional theory calculations.
Original languageEnglish
Article number115144
JournalPhysical Review B
Volume103
Issue number11
DOIs
Publication statusPublished - 24 Mar 2021

Bibliographical note

Funding Information:
Financial support by the Deutsche Forschungsgemeinschaft through TRR80 (project E2) Project No. 107745057 is gratefully acknowledged. A.D.N.J. acknowledges funding and support from the Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/L015544/1. M.S. was partially supported by the Shota Rustaveli Georgian National Science Foundation through the Grant No. FR-19-11872. We would like to thank E. I. Harris-Lee for his insights and also J. Minàr and H. Ebert for a fruitful collaboration.

Publisher Copyright:
© 2021 American Physical Society.

Keywords

  • DFT+DMFT
  • Density functional theory
  • Electronic structure
  • Korringa-Kohn-Rostoker method

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