  The dynamics associated with the time series of the iteration scheme of
coupled cluster theory has been analysed. The phase space analysis indicates
the presence of a few significant cluster amplitudes, mostly involving valence
excitations, which 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 polynomial Kernel Ridge Regression model has been employed to
express each of the enslaved variables uniquely in terms of the master
amplitudes. The subsequent coupled cluster iterations are restricted to a
reduced dimension only to determine those significant excitations, and the
enslaved variables are determined through the already established functional
mapping. We will show that our scheme leads to tremendous reduction in
computational time without sacrificing the accuracy.
