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

Abstract

The iterative solution of the coupled cluster equations exhibits a synergistic relationship among the various 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 an interdependence between