On the applicability of CCSD(T) for dispersion interactions in large conjugated systems

TL;DR

This study evaluates the effectiveness of CCSD(T) in modeling dispersion interactions in large conjugated systems, using the PPP model and benchmarking against higher-level coupled cluster methods, confirming CCSD(T)'s reliability for sizable molecules.

Contribution

It demonstrates that CCSD(T) remains accurate for large conjugated systems and validates the PPP model for studying non-covalent interactions in extensive molecular systems.

Findings

CCSD(T) does not overestimate interaction energies in large systems.

PPP model accurately captures long-range dispersive behavior.

Perturbative triples in CCSD(T) are reliable up to large molecular sizes.

Abstract

In light of the recent discrepancies reported between fixed node diffusion Monte Carlo and local natural orbital coupled cluster with single, double and perturbative triples (CCSD(T)) methodologies for non-covalent interactions in large molecular systems [Al-Hamdani et al., Nat. Comm., 2021, 12, 3927], the applicability of CCSD(T) is assessed using a model framework. The use of the Pariser-Parr-Pople (PPP) model for studying large molecules is critically examined and is shown to recover both bandgap closure as system size increases and long range dispersive behavior of r^-6 with increasing separation between monomers, in corollary with real systems. Using the PPP model, coupled cluster methodologies, CCSDTQ and CCSDT(Q), are then used to benchmark CCSDT and CCSD(T) methodologies for non-covalent interactions in large one- and two-dimensional molecular systems up to the dibenzocoronene dimer. We show that CCSD(T) demonstrates no signs of overestimating the interaction energy for these systems. Furthermore, by examining the Hartree-Fock HOMO-LUMO gap of these large molecules, the perturbative treatment of the triples contribution in CCSD(T) is not expected to cause problems for accurately capturing the interaction energy for system sizes up to at least circumcoronene.