4.6 Article

Sparse modeling of large-scale quantum impurity models with low symmetries

期刊

PHYSICAL REVIEW B
卷 103, 期 4, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.103.045120

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资金

  1. JSPS KAKENHI, Japan [18H01158, 16K17735]
  2. JST, PRESTO, Japan [JPMJPR2012]
  3. JSPS-KAKENHI [18K11345]
  4. Grants-in-Aid for Scientific Research [18K11345] Funding Source: KAKEN

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Quantum embedding theories can be used to describe correlated materials, but an effective impurity model of correlated orbitals embedded in an electron bath poses a challenge. This study proposes an efficient fitting algorithm for matrix-valued hybridization functions to address this issue, demonstrating its effectiveness in fitting hybridization functions and impurity models for high-Tc compounds.
Quantum embedding theories can be used for obtaining quantitative descriptions of correlated materials. However, a critical challenge is solving an effective impurity model of correlated orbitals embedded in an electron bath. Many advanced impurity solvers require the approximation of a bath continuum using a finite number of bath levels, producing a highly nonconvex, ill-conditioned inverse problem. To address this drawback, this study proposes an efficient fitting algorithm for matrix-valued hybridization functions based on a data-science approach, sparse modeling, and a compact representation of Matsubara Green's functions. The efficiency of the proposed method is demonstrated by fitting random hybridization functions with large off-diagonal elements and those of a 20-orbital impurity model for a high-T-c compound, LaAsFeO, at low temperatures (T). The results set quantitative goals for the future development of impurity solvers toward quantum embedding simulations of complex correlated materials.

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