4.7 Article

Solutions of the Two-Dimensional Hubbard Model: Benchmarks and Results from a Wide Range of Numerical Algorithms

Journal

PHYSICAL REVIEW X
Volume 5, Issue 4, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevX.5.041041

Keywords

-

Funding

  1. Simons Foundation
  2. Rice University [NSF-CHE-1462434]
  3. U.S. Department of Energy (DOE) [DE-SC0010530]
  4. PRIN [2010_2010LLKJBX]
  5. National Science Foundation [DMR-1409510]
  6. DOE [DE-SC0008627, ER 46932]
  7. NSFC [11275185]
  8. [DE-SC0008624]
  9. Division Of Chemistry
  10. Direct For Mathematical & Physical Scien [1462434] Funding Source: National Science Foundation
  11. Division Of Materials Research
  12. Direct For Mathematical & Physical Scien [1409510] Funding Source: National Science Foundation
  13. Division Of Physics
  14. Direct For Mathematical & Physical Scien [1314735] Funding Source: National Science Foundation
  15. U.S. Department of Energy (DOE) [DE-SC0010530] Funding Source: U.S. Department of Energy (DOE)

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Numerical results for ground-state and excited-state properties (energies, double occupancies, and Matsubara-axis self-energies) of the single-orbital Hubbard model on a two-dimensional square lattice are presented, in order to provide an assessment of our ability to compute accurate results in the thermodynamic limit. Many methods are employed, including auxiliary-field quantum Monte Carlo, bare and bold-line diagrammatic Monte Carlo, method of dual fermions, density matrix embedding theory, density matrix renormalization group, dynamical cluster approximation, diffusion Monte Carlo within a fixed-node approximation, unrestricted coupled cluster theory, and multireference projected Hartree-Fock methods. Comparison of results obtained by different methods allows for the identification of uncertainties and systematic errors. The importance of extrapolation to converged thermodynamic-limit values is emphasized. Cases where agreement between different methods is obtained establish benchmark results that may be useful in the validation of new approaches and the improvement of existing methods.

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