Large Scale Parallelization in Stochastic Coupled Cluster

Abstract

Coupled cluster theory is a vital cornerstone of electronic structure theory and is being applied to ever-larger systems. Stochastic approaches to quantum chemistry have grown in importance and offer compelling advantages over traditional deterministic algorithms in terms of computational demands, theoretical flexibility or lower scaling with system size. We present a highly parallelizable algorithm of the coupled cluster Monte Carlo method involving sampling of clusters of excitors over multiple time steps. The behaviour of the algorithm is investigated on the uniform electron gas and the water dimer at CCSD, CCSDT and CCSDTQ levels. We also describe two improvements to the original sampling algorithm, full non-composite and multi-spawn sampling. A stochastic approach to coupled cluster results in an efficient and scalable implementation at arbitrary truncation levels in the coupled cluster expansion.

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in "Spencer, J.S. et al., J. Chem. Phys. 149, 204103 (2018)", and may be found at https://doi.org/10.1063/1.5047420. Data and further info can be accessed at https://doi.org/10.17863/CAM.30359

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• Exciting determinants in Quantum Monte Carlo: Loading the dice with fast, low memory weights
• A study of the dense Uniform Electron Gas with high orders of Coupled Cluster
• A Stochastic Approach to Unitary Coupled Cluster
• An optimized twist angle to find the twist-averaged correlation energy applied to the uniform electron gas
• Multireference Stochastic Coupled Cluster
• Accelerating Stochastic Quantum Chemistry
• A hybrid stochastic configuration interaction-coupled cluster approach for multireference systems
• Making the most of data: Quantum Monte Carlo Post-Analysis Revisited
• Stochastic evaluation of four-component relativistic second-order many-body perturbation energies: A potentially quadratic-scaling correlation method