Large scale parallelization in stochastic coupled cluster
J S Spencer1, V A Neufeld2, W A Vigor3
1Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom.
The Journal of Chemical Physics
|December 4, 2018
Summary
Stochastic coupled cluster Monte Carlo methods offer a scalable and efficient alternative to traditional quantum chemistry calculations. This new algorithm improves computational efficiency for large electronic structure problems.
Area of Science:
- Quantum Chemistry
- Computational Physics
- Electronic Structure Theory
Background:
- Coupled cluster theory is fundamental to electronic structure calculations.
- Deterministic algorithms face scalability challenges with increasing system size.
- Stochastic quantum chemistry methods offer advantages in computational demand and scalability.
Purpose of the Study:
- To present a highly parallelizable algorithm for coupled cluster Monte Carlo.
- To investigate the algorithm's performance on model systems.
- To introduce improvements to the sampling methodology.
Main Methods:
- Developed a parallelized coupled cluster Monte Carlo algorithm.
- Employed sampling of excitor clusters over multiple time steps.
- Tested the algorithm on uniform electron gas and water dimer systems.
- Included coupled cluster levels up to quadruple excitations.
Main Results:
- Demonstrated a highly parallelizable and scalable algorithm.
- Investigated performance at various coupled cluster truncation levels.
- Introduced and described 'full non-composite' and 'multi-spawn' sampling improvements.
Conclusions:
- Stochastic coupled cluster methods provide an efficient and scalable approach.
- The presented algorithm enables accurate calculations for larger systems.
- Improvements enhance the practical applicability of coupled cluster Monte Carlo.
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