2020/07/31 by Hiroshi Shinaoka, Yuki Nagai
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Aerospace engineering #Engineering #Geometry #Homogeneous space #Impurity #Magnetic and transport properties of perovskites and related materials #Mathematics #Physics #Physics of Superconductivity and Magnetism #Quantum #Quantum and electron transport phenomena #Quantum mechanics #Scale (ratio) #Scale model #Statistical physics #Theoretical physics #cond-mat.stat-mech #cond-mat.str-el #cond-mat.supr-con
paper · pdf · doi:10.1103/physrevb.103.045120
published as Phys. Rev. B 103, 045120 (2021) · Minor updates from v1, 9 pages including Supplemental Material
arxiv created 2020/08/03 · openalex publication_date 2021/01/19 · arxiv updated 2021/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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-Tc 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.