An Approximate Coupled Cluster Theory via Nonlinear Dynamics and Synergetics: the Adiabatic Decoupling Conditions

TL;DR

This paper introduces an approximate coupled cluster method using nonlinear dynamics and synergetics, exploiting adiabatic decoupling to significantly reduce computational complexity while maintaining high accuracy.

Contribution

It develops a novel adiabatic decoupling approach for coupled cluster equations, drastically reducing degrees of freedom and computational scaling.

Findings

Order of magnitude reduction in computational cost

High accuracy achieved with fewer degrees of freedom

Effective for pilot numerical examples

Abstract

The coupled cluster iteration scheme is analysed as a multivariate discrete-time map using nonlinear dynamics and synergetics. The nonlinearly coupled set of equations to determine the cluster amplitudes are driven by a fraction of the entire set of the cluster amplitudes. These driver amplitudes enslave all other amplitudes through a synergistic inter-relationship, where the latter class of amplitudes behave as the auxiliary variables. The driver and the auxiliary variables exhibit vastly different time scales of relaxation during the iteration process to reach the fixed points. The fast varying auxiliary amplitudes are small in magnitude, while the driver amplitudes are large, and they have a much longer time scale of relaxation. Exploiting their difference in relaxation time-scale, we employ an adiabatic decoupling approximation, where each of the fast relaxing auxiliary modes are expressed as unique functional of the principal amplitudes. This results in a tremendous reduction in the independent degrees of freedom. On the other hand, only the driver amplitudes are determined accurately via exact coupled cluster equations. We will demonstrate that the iteration scheme has an order of magnitude reduction in computational scaling than the conventional scheme. With a few pilot numerical examples, we would demonstrate that this scheme can achieve very high accuracy with significant savings in computational time.