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Related Experiment Videos

Approximate Green's Function Coupled Cluster Method Employing Effective Dimension Reduction.

Bo Peng1, Roel Van Beeumen2, David B Williams-Young2

  • 1William R. Wiley Environmental Molecular Sciences Laboratory, Battelle , Pacific Northwest National Laboratory , K8-91, P.O. Box 999, Richland , Washington 99352 , United States.

Journal of Chemical Theory and Computation
|April 6, 2019
PubMed
Summary
This summary is machine-generated.

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Model-order reduction (MOR) techniques significantly reduce the computational cost of the Green's function coupled cluster (GFCC) method for electronic structure calculations. This approach enhances efficiency and accuracy for strongly correlated systems.

Area of Science:

  • Computational Chemistry
  • Quantum Many-Body Physics

Background:

  • The Green's function coupled cluster (GFCC) method is a powerful tool for electronic structure analysis, particularly for strongly correlated systems.
  • High computational cost currently limits the routine application of GFCC methods.
  • Solving GFCC equations in the frequency domain using iterative solvers offers parallelization benefits.

Purpose of the Study:

  • To apply model-order-reduction (MOR) techniques within the GFCC framework to decrease computational overhead.
  • To develop a more efficient and routineable GFCC method for electronic structure calculations.

Main Methods:

  • Implemented MOR by projecting the full-dimensional GFCC linear system onto a reduced-dimension subspace.
  • Constructed the subspace iteratively using auxiliary vectors from GFCC linear equations at selected frequencies.

Related Experiment Videos

  • Validated the approach on molecular systems like CO, 1,3-butadiene, benzene, and adenine.

    • Main Results:

    • MOR successfully approximated GFCC linear equations in both interpolative and extrapolative spectral regions.
    • Achieved significant reductions in computational cost for molecular systems while maintaining accuracy.
    • The reduced-order model provided a high-quality initial guess, improving iterative solver convergence.

    Conclusions:

    • MOR is an effective technique for reducing the computational cost of GFCC calculations.
    • This method enhances the practicality of GFCC for analyzing electronic structures of complex molecular systems.
    • The MOR-GFCC approach offers a promising path towards routine electronic structure computations.