hamiltonian and then promote the MF parameters to classical variables studied via MC simulations, while fermions are exactly diagonalized in the background of those classical variables. We present the Hubbard U vs. temperature phase diagram on large three and two dimensional clusters. Our MC-MF method can capture the nonmonotonicity of TN with U, local moment physics above TN, and the two peak behavior of specic heat, as compared with Determinantal Quantum Monte Carlo
hamiltonian and then promote the MF parameters to classical variables studied via MC simulations, while fermions are exactly diagonalized in the background of those classical variables. We present the Hubbard U vs. temperature phase diagram on large three and two dimensional clusters. Our MC-MF method can capture the nonmonotonicity of TN with U, local moment physics above TN, and the two peak behavior of specic heat, as compared with Determinantal Quantum Monte Carlo
The canonical one-band Hubbard model is studied using a computational method that mixes the Monte Carlo procedure with the mean field approximation. This technique allows us to incorporate thermal fluctuations and the development of short-range magnetic order above ordering temperatures, contrary to the crude finite-temperature Hartree-Fock approximation, which incorrectly predicts a N\'eel temperature TN that grows linearly with the Hubbard U/t. The effective model studied here contains quantum and classical degrees of freedom. It thus belongs to the ``spin fermion'' model family widely employed in other contexts. Using exact diagonalization, supplemented by the traveling cluster approximation, for the fermionic sector, and classical Monte Carlo for the classical fields, the Hubbard U/t vs temperature T/t phase diagram is studied employing large three- and two-dimensional clusters. We demonstrate that the method is capable of capturing the formation of local moments in the normal state without long-range order, the nonmonotonicity of TN with increasing U/t, the development of gaps and pseudogaps in the density of states, and the two-peak structure in the specific heat. Extensive comparisons with determinant quantum Monte Carlo results suggest that the present approach is qualitatively, and often quantitatively, accurate, particularly at intermediate and high temperatures. Finally, we study the Hubbard model including plaquette diagonal hopping (i.e., the $t\text{\ensuremath{-}}{t}^{\ensuremath{'}}$ Hubbard model) in two dimensions and show that our approach allows us to study low-temperature properties where determinant quantum Monte Carlo fails due to the fermion sign problem. Future applications of this method include multiorbital Hubbard models such as those needed for iron-based superconductors.
Almost 50 years after the invention of the Hubbard model [1–3] and despite modern petaflop supercomputers, a precise analysis of the criticality of this most basic model for electronic correlations has not been achieved so far, at least not in three dimensions. Dynamical mean field theory (DMFT) [4–6] was a big step forward to quantitatively calculate the three dimensional Hubbard model since the major contribution of electronic correlations, i.e., the local ones, are well captured within this theory. Local correlations give rise to quasiparticle renormalization, the Mott-Hubbard transition, magnetism, and even more subtle issues such as kinks in a purely electronic models [7]. However, non-local spatial correlations are also naturally generated by a purely local Hubbard interaction, and, as it is well known, they become of essential importance in the vicinity of secondorder phase transitions. As these correlations are neglected in DMFT, this scheme can provide only for a conventional mean-field (MF) description of the critical properties. To overcome this shortcoming cluster extensions to DMFT such as the dynamical cluster approximation (DCA) and cluster-DMFT have been proposed, see Ref. 8 for a review. In these approaches non-local correlations beyond DMFT are taken into account, however only within the range of the cluster size; and due to computational limitations the actual size of the cluster is severely restricted to about 3-5 sites for each direction. Hence, short-range correlations are included by these approaches, whereas long-range ones are not. Nonetheless, Kent et al. [9] were able to extrapolate the cluster size of so-called Betts clusters to infinity, albeit assuming from the very beginning the critical exponents to be those of the Heisenberg model. This way they extrapolated the critical temperature of the paramagnetic-toantiferromagnetic phase transition which was found to be in agreement with earlier quantum Monte Carlo (QMC) simulations of Staudt et al. [10] on finite lattices.
This thesis will describe efforts to enhance our ability to simulate the 2D Hubbard model. Chapter 2 provides a background on the main computational techniques used throughout the work, including quantum Monte Carlo (QMC), dynamical mean field theory (DMFT), the dynamical cluster approximation (DCA), the continuous time auxiliary field algorithm (CTAUX), and the Maximum Entropy Method (MEM) for numeric analytic continuation. Chapter 3 presents new work on applying Twisted Boundary Conditions to the DCA framework. This method is applied in a effort to access thermodynamic (i.e. large system) information about the Hubbard model without the immense computational expense required to simulate large lattices directly. Chapter 4 describes efforts to study the extended 2D Hubbard model, which re-introduces non-local interactions between electrons that drive the formation of charge ordered phases. The work includes a thorough analysis of the phase diagram of the model away from half-filling, as well as analysis of the effect of non-local interactions on anti-ferromagnetic fluctuations and competition between the charge order and AFM states. Chapter 5 includes a derivation of the dual fermions diagrammatic expansion and the dual fermions ladder approximation used to compute corrections to single site DMFT calculations. The method is applied to the 2D Hubbard model in order to study the evolution of its spectral function, Fermi surface, and momentum dependent mass renormalization as the temperature, interaction strength, and doping are changed. The great advantage of this method is that we are able to access arbitrary momenta throughout the Brillouin zone, in contrast to the limitation to a few cluster momenta that characterize cluster methods like DCA.
Richard T. Scalettar Physics Department, University of California, Davis, California 95616, U.S.A. George G. Batrouni INLN, Universite´ de Nice - Sophia Antipolis, CNRS; 1361 route des Lucioles, 06560 Valbonne, France Originally introduced almost fifty years ago, the Hubbard Hamiltonian, H = −t ∑ 〈ij〉 ∑ i (ni↑− 12)(ni↓− 1 2 )−μ ∑ i (ni↑+ni↓), (11.1) has been widely, and very successfully, used to model many-body effects and quantum phase transitions (QPTs) in condensed matter, despite the simplifications it embodies [1-4]; see also Chap. 1. The Hamiltonian, given by Eq. (11.1), contains a kinetic energy term t which describes the hopping of two sets of fermionic particles, labeled by σ, on near-neighbor sites 〈ij〉 of the lattice.1 These fermions feel an interaction energy U if they occupy the same site, a term written here in a symmetric form which makes chemical potential μ = 0 correspond to half-filling ρ = 〈∑σ niσ〉 = 1 at any value of t, U or temperature T , as long as the lattice is bipartite. Many different geometries have been studied, including 1D chains, 2D square, triangular, and honeycomb structures, and 3D cubic lattices.
Cluster Perturbation Theory (CPT) is a technique for computing the spectral function of fermionic models with local interactions. By combining the solution of the model on a finite cluster with perturbation theory on intra-cluster hoppings, CPT provides access to single-particle properties with arbitrary momentum resolution while incurring low computational cost. Here, we introduce Determinantal Quantum Monte Carlo (DQMC) as a solver for CPT. Compared to the standard solver, exact diagonalization (ED), the DQMC solver reduces finite size effects through utilizing larger clusters, allows study of temperature dependence, and enables large-scale simulations of a greater set of models. We discuss the implementation of the DQMC solver for CPT and benchmark the CPT+DQMC method for the attractive and repulsive Hubbard models, showcasing its advantages over standard DQMC and CPT+ED simulations.