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arXiv:1907.07191 · Physical review. B./Physical review. B

Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation

Carlos Mejuto-Zaera, Leonardo Zepeda-Núñez, Michael Lindsey, Norm M. Tubman, K. Birgitta Whaley, Lin Lin

Published Jan 27, 2020• 39 citations
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Abstract

We introduce a nested optimization procedure using semidefinite relaxation for the fitting step in Hamiltonian-based cluster dynamical mean-field theory methodologies. We show that the proposed method is more efficient and flexible than state-of-the-art fitting schemes, which allows us to treat as large a number of bath sites as the impurity solver at hand allows. We characterize its robustness to initial conditions and symmetry constraints, thus providing conclusive evidence that in the presence of a large bath our semidefinite relaxation approach can find the correct set of bath parameters without needing to include a priori knowledge of the properties that are to be described. We believe this method will be of great use for Hamiltonian-based calculations, simplifying and improving one of the key steps in cluster dynamical mean-field theory calculations.

DOI
10.1103/physrevb.101.035143

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We analyze cellular dynamical mean-field theory (CDMFT) and the dynamical cluster approximation (DCA). We derive exact sum-rules for the hybridization functions and give examples for dynamical mean-field theory, CDMFT, and DCA. For impurity solvers based on a Hamiltonian, these sum rules can be used to monitor convergence of the bath-parametrization. We further discuss how the symmetry of the cluster naturally leads to a decomposition of the bath Green matrix into irreducible components, which can be parametrized independently, and give an explicit recipe for finding the optimal bath parametrization. As a benchmark we revisit the one-dimensional Hubbard model. We carefully analyze the evolution of the density as a function of chemical potential and find that, close to the Mott transition, convergence with cluster size is unexpectedly slow. Going from one to two dimensions we find that fitting the bath becomes in general significantly more difficult, requiring a large number of bath sites. For such large baths our symmetry-adapted approach should prove crucial for finding a reliable bath-parametrization.
We here present how a self-consistent solution of the dynamical mean field theory equations can be obtained using exact diagonalization of an Anderson impurity model with accuracies comparable to those found using renormalization group or quantum Monte Carlo methods. We show how one can solve a correlated quantum impurity coupled to several hundred uncorrelated bath sites, using a restricted active basis set. The number of bath sites determines the resolution of the obtained spectral function, which consists of peaks roughly spaced by the band width divided by the number of bath sites. The self-consistency cycles are fully performed on the real frequency axis and expressed as numerical stable matrix operations. The same impurity solver has been used on Ligand Field and finite size cluster calculations and is capable of treating involved Hamiltonians including the full rotational invariant Coulomb interaction, spin-orbit coupling and low symmetry crystal-fields. The proposed method allows for the calculation of a variety of correlation functions at little extra cost.
Cluster Dynamical Mean-Field Theory (CDMFT) with an Exact Diagonalization (ED) impurity solver faces exponential scaling limitations from the Hilbert space dimension. We introduce Subbath CDMFT (SB-CDMFT), an alternative to the conventional ED-CDMFT method in which the discrete bath is subdivided into separate subbaths, each coupled to the cluster with distinct hybridization functions. In this approach, only one subbath at a time is actively involved in ED, dramatically reducing the computational cost by replacing a single large impurity problem with multiple smaller separate ones. Our method successfully reproduces key physical properties including particle-hole symmetry and Mott physics, while allowing for an extended bath representation at a fraction of the typical computational cost.
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We demonstrate an algebraic construction of frequency-dependent bath orbitals which can be used in a robust and rigorously self-consistent DMFT-like embedding method, here called $\ensuremath{\omega}$-DMFT, suitable for use with Hamiltonian-based impurity solvers. These bath orbitals are designed to exactly reproduce the hybridization of the impurity to its environment, while allowing for a systematic expansion of this bath space as impurity interactions couple frequency points. In this way, the difficult nonlinear fit of bath parameters necessary for many Hamiltonian-formulation impurity solvers in DMFT is avoided, while the introduction of frequency dependence in this bath space is shown to allow for more compact bath sizes. This has significant potential use with a number of new, emerging Hamiltonian solvers which allow for the embedding of large impurity spaces within a DMFT framework. We present results of the $\ensuremath{\omega}$-DMFT approach for the Hubbard model on the Bethe lattice, a one-dimensional chain, and the two-dimensional square lattice, which show excellent agreement with standard DMFT results, with fewer bath orbitals and more compact support for the hybridization representation in the key impurity model of the method.