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Coupled Cluster as an Impurity Solver for Green's Function Embedding Methods.

Avijit Shee1, Dominika Zgid1

  • 1Department of Chemistry , University of Michigan , Ann Arbor , Michigan 48109 , United States.

Journal of Chemical Theory and Computation
|September 14, 2019
PubMed
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We developed an efficient Green's function coupled cluster singles and doubles (CCSD) solver for embedding methods. This new solver shows excellent agreement with FCI for 1D models and is promising for complex systems.

Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Materials Science

Background:

  • Green's function methods are crucial for describing electron correlation in materials.
  • Coupled cluster (CC) methods offer high accuracy but can be computationally expensive.
  • Efficient solvers are needed for Green's function embedding theories.

Purpose of the Study:

  • To evaluate the performance of Green's function coupled cluster singles and doubles (CCSD) as a solver for Green's function embedding methods.
  • To develop an efficient CC solver with frequency-independent scaling.
  • To assess the accuracy of the Green's function CCSD solver across different correlation regimes and system sizes.

Main Methods:

  • Constructed a one-particle Green's function from the CC wave function using the non-Hermitian Lanczos algorithm.

Related Experiment Videos

  • Applied the Green's function CCSD solver to half-filled 1D and 2D Hubbard models with impurity problems.
  • Compared results with full configuration interaction (FCI) and analyzed performance within Self-Energy Embedding Theory (SEET).

    • Main Results:

    • The Green's function CCSD solver demonstrated excellent agreement with FCI for the 1D Hubbard model across all interaction strengths.
    • For the 2D Hubbard model, discrepancies with FCI were observed in the strongly correlated regime using an open-shell implementation.
    • Analysis on an ammonia cluster showed the solver's performance within SEET compared to Hartree-Fock (HF) and Green's Function second order (GF2).

    Conclusions:

    • The Green's function CCSD solver is a computationally efficient and accurate method for Green's function embedding, particularly in weakly to moderately correlated systems.
    • The frequency-independent scaling offers a significant advantage for computational cost.
    • Further development may be needed to address strong correlation effects in larger, more complex systems.