The use of many control variates is proposed as a method to accelerate the second- and third-order Monte Carlo (MC) many-body perturbation (MC-MP2 and MC-MP3) calculations. A control variate is an exactly integrable function that is strongly correlated or anti-correlated with the target function to be integrated by the MC method. Evaluating both integrals and their covariances in the same MC run, one can effect a mutual cancellation of the statistical uncertainties and biases in the MC integrations, thereby accelerating its convergence considerably. Six and thirty-six control variates, whose integrals are known a priori, are generated for MC-MP2 and MC-MP3, respectively, by systematically replacing one or more two-electron-integral vertices of certain configurations by zero-valued overlap-integral vertices in their Goldstone diagrams. The variances and covariances of these control variates are computed at a marginal cost, enhancing the overall efficiency of the MC-MP2 and MC-MP3 calculations by a factor of up to 14 and 20, respectively.
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7 September 2020
Research Article|
September 03 2020
Convergence acceleration of Monte Carlo many-body perturbation methods by using many control variates
Special Collection:
Frontiers of Stochastic Electronic Structure Calculations
Alexander E. Doran
;
Alexander E. Doran
a)
Department of Chemistry, University of Illinois at Urbana-Champaign
, Urbana, Illinois 61801, USA
a)Author to whom correspondence should be addressed: [email protected]
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Alexander E. Doran
a)
So Hirata
b)
Department of Chemistry, University of Illinois at Urbana-Champaign
, Urbana, Illinois 61801, USA
a)Author to whom correspondence should be addressed: [email protected]
b)
Electronic mail: [email protected]
Note: This paper is part of the JCP Special Topic on Frontiers of Stochastic Electronic Structure Calculations.
J. Chem. Phys. 153, 094108 (2020)
Article history
Received:
July 01 2020
Accepted:
August 17 2020
Citation
Alexander E. Doran, So Hirata; Convergence acceleration of Monte Carlo many-body perturbation methods by using many control variates. J. Chem. Phys. 7 September 2020; 153 (9): 094108. https://doi.org/10.1063/5.0020584
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