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The iteration dynamics of the coupled cluster equations exhibits asynergistic relationship among the cluster amplitudes. The iteration scheme maybe viewed as a multivariate discrete-time propagation of nonlinearly coupledequations, which is dictated by only a few principal cluster amplitudes. Theseprincipal amplitudes usually correspond to only a few valence excitations,whereas all other cluster amplitudes are enslaved, and behave as auxiliaryvariables. Staring with a few trial iterations, we employ a supervised machinelearning strategy to establish a mapping of the principal and auxiliaryamplitudes. We introduce a machine learning-coupled cluster hybrid scheme wherethe coupled cluster equations are solved only to determine the principalamplitudes, which saves significant computation time. The auxiliary amplitudes,on the other hand, are determined via regression. Few different regressiontechniques have been introduced to express the auxiliary amplitudes asfunctions of the principal amplitudes. The scheme has been applied to severalmolecules in their equilibrium and stretched geometries, and our scheme, withboth the regression models, shows a significant reduction in computation timewithout unduly sacrificing the accuracy.

Assessing the Performance of Nonlinear Regression based Machine Learning Models to Solve Coupled Cluster Theory