Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Journal of the Optical Society of America A

Cluster generalizations of dynamical coherent potential approximation (A)

Roberto E. Lagos, Richard A. Friesner

Published Jan 1, 1983• 0 citations
OpenAlex

Related papers

Cluster generalization of the coherent-potential approximation on the basis of the projection formalism in an augmented space

А. К. Аржников, S. G. Novokshonov

Theoretical and Mathematical Physics•Jul 1, 1990•1 citation

Hubbard models on general lattices: A dynamical cluster approximation approach

Jakub Imriška

•Jan 1, 2016•0 citations

Towards Analytical Approaches to the Dynamical-Cluster Approximation

J. P. Hague

arXiv:0912.0698•Journal of Low Temperature Physics•Dec 15, 2009•0 citations

Two Quantum Cluster Approximations

Thomas Maier, O. Gonzalez, M. Jarrell +1 more

We provide microscopic diagrammatic derivations of the Molecular Coherent Potential Approximation (MCA) and Dynamical Cluster Approximation (DCA) and show that both are Phi-derivable. The MCA (DCA) maps the lattice onto a self-consistently embedded cluster with open (periodic) boundary conditions, and therefore violates (preserves) the translational symmetry of the original lattice. As a consequence of the boundary conditions, the MCA (DCA) converges slowly (quickly) with corrections O(1/Lc) (O(1/Lc^2)), where Lc is the linear size of the cluster. However, local quantities, when measured in the center of the MCA cluster, converge more quickly than the DCA result. These results are demonstrated numerically for the one-dimensional symmetric Falicov-Kimball model.

arXiv:cond-mat/0205460•May 22, 2002•1 citation

Coherent-Potential Approximations

WORLD SCIENTIFIC eBooks•Oct 1, 2012•0 citations

Comparison of two-quantum-cluster approximations

Thomas Maier, Mark Jarrell

We provide microscopic diagrammatic derivations of the molecular coherent potential approximation (MCA) and dynamical cluster approximation (DCA) and show that both are $\ensuremath{\Phi}$ derivable. The MCA (DCA) maps the lattice onto a self-consistently embedded cluster with open (periodic) boundary conditions, and therefore violates (preserves) the translational symmetry of the original lattice. As a consequence of the boundary conditions, the MCA (DCA) converges slowly (quickly) with corrections $\mathcal{O}{(1/L}_{c})$ $[\mathcal{O}{(1/L}_{c}^{2})],$ where ${L}_{c}$ is the linear size of the cluster. These analytical results are demonstrated numerically for the one-dimensional symmetric Falicov-Kimball model.

arXiv:cond-mat/0110192•Physical review. B, Condensed matter•Jan 3, 2002•25 citations
We provide microscopic diagrammatic derivations of the Molecular Coherent Potential Approximation (MCA) and Dynamical Cluster Approximation (DCA) and show that both are Phi-derivable. The MCA (DCA) maps the lattice onto a self-consistently embedded cluster with open (periodic) boundary conditions, and therefore violates (preserves) the translational symmetry of the original lattice. As a consequence of the boundary conditions, the MCA (DCA) converges slowly (quickly) with corrections O(1/Lc) (O(1/Lc^2)), where Lc is the linear size of the cluster. However, local quantities, when measured in the center of the MCA cluster, converge more quickly than the DCA result. These results are demonstrated numerically for the one-dimensional symmetric Falicov-Kimball model.
We provide microscopic diagrammatic derivations of the molecular coherent potential approximation (MCA) and dynamical cluster approximation (DCA) and show that both are $\ensuremath{\Phi}$ derivable. The MCA (DCA) maps the lattice onto a self-consistently embedded cluster with open (periodic) boundary conditions, and therefore violates (preserves) the translational symmetry of the original lattice. As a consequence of the boundary conditions, the MCA (DCA) converges slowly (quickly) with corrections O(1/Lc)\mathcal{O}{(1/L}_{c})O(1/Lc​) [O(1/Lc2)],[\mathcal{O}{(1/L}_{c}^{2})],[O(1/Lc2​)], where Lc{L}_{c}Lc​ is the linear size of the cluster. These analytical results are demonstrated numerically for the one-dimensional symmetric Falicov-Kimball model.