Quantum Monte Carlo Evidence for d-wave Pairing in the 2D Hubbard Model at a van Hove Singularity

We implement a Quantum Monte Carlo calculation for a repulsive Hubbard model with nearest and next-nearest neighbor hopping interactions on clusters up to 12x12. A parameter region where the Fermi level lies close to the van Hove singularity at the Saddle Points in the bulk band structure is investigated. A pairing tendency in the $d_{x^2-y^2}$ symmetry channel, but no other channel, is found. Estimates of the effective pairing interaction show that it is close to the value required for a 40 K superconductor. Finite-size scaling compares with the attractive Hubbard model.Comment: 11 pages, REVTex, 4 figures, postscrip

The Effect Of Particle Shape And Friction On The Stresses In Heaps Of Granular Media

this paper is to include more physical features like friction and non-spherical shapes of the particles in the model by computational means and to investigate the effect of different construction methods and boundary conditions. 2 Algorith

Double layer Hubbard model: off-diagonal long range order in the nodeless d-wave channel

We report on a numerical study of the double layer Hubbard model describing the CuO 2 sheets in the high- $T_{\rm c}$ oxides. For the simulation we employed the projector quantum Monte Carlo method (PQMC) to study the ground state properties of this correlated electron system. Our results provide evidence for off-diagonal long range order in the nodeless d-wave channel. The implications with respect to possible mechanisms for high- $T_{\rm c}$ superconductivity in oxides in combination which phononic booster mechanisms are discussed

Numerical simulation of the double layer Hubbard model

We have carried out Quantum Monte Carlo studies to investigate the double layer Hubbard model using the groundstate projector formalism. Evidence for off diagonal long range order in the nodeless d-wave pairing channel is provided. It is emphasized that the Projector Quantum Monte Carlo method has the potential to detect superconducting mechanisms in strongly correlated electron systems, which are not treatable by standard analytic methods. The Monte Carlo technique provides useful information about systems much larger than those accessible for exact diagonalization methods

Efficient parallelization of the 2D Swendsen-Wang algorithm

We established a fast Swendsen-Wang algorithm for the two-dimensional Ising model on parallel computers with a high efficiency. On an Intel paragon with 140 processors we reached spin update times of only 14 ns with an efficiency of 89%. This algorithm was used to examine the non-equilibrium relaxation of magnetization and energy in large Ising systems of a size up to 17920 × 17920 spins. Nevertheless we observed still a strong finite-size effect for the magnetization. We assume both magnetization and energy decay to behave like (t + Δ)-λe-bt in an infinitely large system. Thus, for long times magnetization and energy show an exponential, asymtotic time-dependence, implying a critical dynamic exponent z of zero

Experiences with re-engineering and parallelizing a high-Tc superconductivity code

Motivated by experiments on high-Tc superconducting compounds and made possible by the power of present day supercomputers, a number of researchers have used the Quantum Monte Carlo method to gain more insight into Hubbard-like many particle models. We used the Projector Quantum Monte Carlo method (PQMC) [1,2] as a computational technique for studying the physical properties of many-electron and quantum-spin systems. This review gives a survey of the algorithms and techniques as well as of the methods to implement the PQMC scheme on modern parallel computers like the Intel Hypercube or IBM RS/6000 workstation clusters. In order to study systems of larger size or with more complex interactions, increasingly large amounts of computational power are required. Although based on one particular algorithm, we believe that our results should interest simulators with quite different algorithms and other parallel architectures as well