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Charles J C Scott1, Roberto Di Remigio2,3, T Daniel Crawford3

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We introduce diagCCMC, a novel coupled cluster Monte Carlo method. This approach significantly reduces memory usage for quantum chemistry calculations, enabling linear scaling for noninteracting systems.

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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Stochastic Methods

Background:

  • Coupled cluster (CC) methods are essential for accurate electronic structure calculations.
  • Stochastic coupled cluster Monte Carlo (CCMC) offers a path to larger systems but faces memory challenges.
  • Existing CCMC methods struggle with memory scaling, limiting their applicability.

Purpose of the Study:

  • To develop a modified CCMC algorithm with reduced memory footprint.
  • To enable accurate quantum chemical calculations for larger and more complex systems.
  • To bridge the gap between stochastic and deterministic CC approaches.

Main Methods:

  • Developed diagCCMC, a novel stochastic algorithm for sampling connected terms in CC expansions.
  • Constructed coupled cluster diagrams on the fly for efficient computation.
  • Implemented propagation using only connected components of the similarity-transformed Hamiltonian.

Main Results:

  • diagCCMC achieves linear memory scaling for noninteracting systems.
  • Significant memory reduction observed with system dissociation (e.g., helium chains).
  • Method remains robust in the presence of strong correlation (e.g., stretched nitrogen molecule).

Conclusions:

  • diagCCMC offers a substantial reduction in memory cost for stochastic CC calculations.
  • The method demonstrates favorable scaling and robustness across various system types.
  • This advancement brings stochastic CCMC closer to the accuracy and efficiency of deterministic methods.