  The iteration dynamics of the coupled cluster equations exhibits a
synergistic relationship among the cluster amplitudes. The iteration scheme may
be viewed as a multivariate discrete-time propagation of nonlinearly coupled
equations, which is dictated by only a few principal cluster amplitudes. These
principal amplitudes usually correspond to only a few valence excitations,
whereas all other cluster amplitudes are enslaved, and behave as auxiliary
variables. Staring with a few trial iterations, we employ a supervised machine
learning strategy to establish a mapping of the principal and auxiliary
amplitudes. We introduce a machine learning-coupled cluster hybrid scheme where
the coupled cluster equations are solved only to determine the principal
amplitudes, which saves significant computation time. The auxiliary amplitudes,
on the other hand, are determined via regression. Few different regression
techniques have been introduced to express the auxiliary amplitudes as
functions of the principal amplitudes. The scheme has been applied to several
molecules in their equilibrium and stretched geometries, and our scheme, with
both the regression models, shows a significant reduction in computation time
without unduly sacrificing the accuracy.
