2017/09/30 by Johan Carlström
Materials Science · Mathematics · Physics and Astronomy · #Advanced Condensed Matter Physics #Computer science #Convergence (economics) #Diagrammatic reasoning #Hubbard model #Lattice (music) #Magnetic and transport properties of perovskites and related materials #Mathematics #Monte Carlo method #Physics #Physics of Superconductivity and Magnetism #Quantum mechanics #Statistical physics #cond-mat.str-el #msc:81T80
paper · pdf · doi:10.1103/physrevb.97.075119
published as Phys. Rev. B 97, 075119 (2018) · 10 pages and 7 figures
openalex created_date 2017/09/25 · arxiv created 2018/01/26 · openalex publication_date 2018/02/12 · arxiv updated 2018/02/21 · openalex updated_date 2026/08/05
Using a dual representation of lattice fermion models that is based on spin-charge transformation and fermionization of the original description, I derive an algorithm for diagrammatic Monte Carlo simulation of strongly correlated systems. This scheme allows eliminating large expansion parameters, as well as large corrections to the density matrix that generally prevent diagrammatic methods from being efficient in this regime. As an example, I compute the filling factor for the Hubbard model at infinite on-site repulsion and compare the results to controllable data obtained from numerical linked-cluster expansion. I find excellent agreement between the two methods, as well as rapid convergence of the diagrammatic series. I also report results for the momentum distribution and kinetic energy of the electrons.