Accelerating coupled cluster calculations with nonlinear dynamics and supervised machine learning

In this paper, the iteration scheme associated with single reference coupled cluster theory has been analyzed using nonlinear dynamics. The phase space analysis indicates the presence of a few significant cluster amplitudes, mostly involving valence excitations, that dictate the dynamics, while all...

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Bibliographic Details
Published in:The Journal of chemical physics 2021-01, Vol.154 (4), p.044110-044110
Main Authors: Agarawal, Valay, Roy, Samrendra, Chakraborty, Anish, Maitra, Rahul
Format: Article
Language:English
Subjects:
Amplitudes
Clusters
Computing time
Excitation
Iterative methods
Machine learning
Nonlinear analysis
Nonlinear dynamics
Polynomials
Regression analysis
Regression models
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Summary:In this paper, the iteration scheme associated with single reference coupled cluster theory has been analyzed using nonlinear dynamics. The phase space analysis indicates the presence of a few significant cluster amplitudes, mostly involving valence excitations, that dictate the dynamics, while all other amplitudes are enslaved. Starting with a few initial iterations to establish the inter-relationship among the cluster amplitudes, a supervised machine learning scheme with a polynomial kernel ridge regression model has been employed to express each of the enslaved amplitudes uniquely in terms of the former set of amplitudes. The subsequent coupled cluster iterations are restricted solely to determine those significant excitations, and the enslaved amplitudes are determined through the already established functional mapping. We will show that our hybrid scheme leads to a significant reduction in the computational time without sacrificing the accuracy.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0037090