Factorized Quadruples and a Predictor of Higher-Level Correlation in Thermochemistry (2407.03519v1)
Abstract: Coupled cluster theory has had a momentous impact on the ab initio prediction of molecular properties, and remains a staple ingratiate in high-accuracy thermochemical model chemistries. However, these methods require inclusion of at least some connected quadruple excitations, which generally scale at best as $\mathcal{O}(N9)$ with the number of basis functions. It very difficult to predict, a priori, the effect correlation past CCSD(T) has on a give reaction energies. The purpose of this work is to examine cost-effective quadruple corrections based on the factorization theorem of many-body perturbation theory that may address these challenges. We show that the $\mathcal{O}(N7)$, factorized CCSD(TQ${}\text{f}$) method introduces minimal error to predicted correlation and reaction energies as compared to the $\mathcal{O}(N9)$ CCSD(TQ). Further, we examine the performance of Goodson's continued fraction method in the estimation of CCSDT(Q)${}\Lambda$ contributions to reaction energies, as well as a "new" method related to %TAE[(T)] that we refer to as a scaled perturbation estimator. We find that the scaled perturbation estimator based upon CCSD(TQ${}\text{f}$)/cc-pVDZ is capable of predicting CCSDT(Q)${}\Lambda$/cc-pVDZ contributions to reaction energies with an average error of 0.07 kcal mol${}{-1}$ and a RMST of 0.52 kcal mol${}{-1}$ when applied to a test-suite of nearly 3000 reactions. This offers a means by which to reliably ballpark how important post-CCSD(T) contributions are to reaction energies while incurring no more than CCSD(T) formal cost and a little mental math.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.