Searcharxiv⌕ Search

arXiv · cond-mat/0209170

Dynamical Cluster Approximation Employing FLEX as a Cluster Solver

Abstract

We employ the Dynamical Cluster Approximation (DCA) in conjunction with the Fluctuation Exchange Approximation (FLEX) to study the Hubbard model. The DCA is a technique to systematically restore the momentum conservation at the internal vertices of Feynman diagrams relinquished in the Dynamical Mean Field Approximation (DMFA). FLEX is a perturbative diagrammatic approach in which classes of Feynman diagrams are summed over analytically using geometric series. The FLEX is used as a tool to investigate the complementarity of the DCA and the finite size lattice technique with periodic boundary conditions by comparing their results for the Hubbard model. We also study the microscopic theory underlying the DCA in terms of compact (skeletal) and non-compact diagrammatic contributions to the thermodynamic potential independent of a specific model. The significant advantages of the DCA implementation in momentum space suggests the development of the same formalism for the frequency space. However, we show that such a formalism for the Matsubara frequencies at finite temperatures leads to acausal results and is not viable. However, a real frequency approach is shown to be feasible.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

K. Aryanpour, M. H. Hettler, M. Jarrell. 2002-12-06. Dynamical Cluster Approximation Employing FLEX as a Cluster Solver. https://doi.org/10.1103/physrevb.67.085101

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Point-Group Equivariant Neural Networks for Local Dynamics in Lattice Systems

We develop point-group equivariant neural networks for learning local mappings used in dynamical simulations of lattice systems. The same network acts on a finite neighborhood around each site, respecting lattice translations through shared parameters. The local configuration is decomposed into features that transform according to irreducible representations of the point group, and hidden features retain this organization throughout the network. Equivariant linear maps and symmetry-allowed tensor products then learn nonlinear spatial correlations while preserving the required transformation laws. This approach avoids constructing a fixed set of hand-crafted invariant descriptors, whose selection requires choices about which local correlations to retain. It also allows the complexity of the learned mapping to grow without enlarging its spatial cutoff. We demonstrate the construction by learning the forces governing adiabatic dynamics in the square-lattice Holstein model. Comparisons with exact diagonalization test the accuracy of the predicted forces and the evolution of spatial correlations. Force evaluation scales linearly with system size, allowing the trained model to simulate charge-density-wave ordering on large lattices. These simulations reproduce anomalously slow coarsening of the charge-density-wave domains, illustrating the use of the learned local mapping for collective dynamics.

cond-mat.str-el↗

Topological Chiral Superconductivity from Doping Correlated Charge Transfer Insulator with Orbital Magnetism

Topological chiral superconductivity has been extensively studied from the perspective of electronic structures in momentum space, while its emergence from real-space processes in strongly interacting systems remains largely unexplored. Here, we study a doped charge-transfer insulator in a strongly repulsive spinless-fermion model with three sublattices and $2π$ flux per unit cell. Combining perturbative analysis, exact diagonalization, and density matrix renormalization group simulations, we establish a topological superconducting ground state with $p+ip$ symmetry. In the large charge-transfer gap limit, chiral charge-transfer excitations generate a correlated-hopping process that provides a microscopic mechanism for electron pairing and its chiral structure. The chiral superconductivity persists into the smaller-gap regime, where chiral excitonic two-particle bound state can emerge. Our results establish a concrete real-space route to topological chiral superconductivity upon doping strongly interacting charge-transfer insulators with orbital magnetism.

cond-mat.str-el↗

Solving fractional quantum Hall problems with thermodynamic-limit bootstrap

Determining correlated ground states directly in the thermodynamic limit remains a central challenge in quantum many-body physics. Here we develop a quantum many-body bootstrap that yields a rigorous and tight lower bound on the thermodynamic ground-state energy of a fractional quantum Hall system together with an optimized reduced density matrix $ρ_W^\star$ on a spatial window. For sufficiently tight bounds, we find that $ρ_W^\star$ accurately reproduces the ground-state reduced density matrix, as directly tested by its fidelity with known model states. For exactly solvable Laughlin and Moore--Read parent Hamiltonians, the bootstrap also reproduces pair correlations and orbital entanglement spectra (OES). For Landau-level-projected Coulomb interactions across multiple fillings, the energy bounds closely track established thermodynamic estimates, while the window density matrix recovers the familiar $ν=1/3$ Laughlin and $ν=5/2$ Moore--Read states and the composite Fermi liquid at $ν=1/2$. More importantly, the same framework also resolves unsettled regimes. At $ν=1/4$, the OES and real-space correlations identify a composite Fermi liquid, while at $ν=12/5$ the combined diagnostics suggest $\overline{\mathrm{RR}}_3$ as the ground-state order. Our bootstrap therefore provides direct access to both ground-state energies and many-body structure in the thermodynamic limit.

cond-mat.str-el↗