Orbital pair selection for relative energies in the domain-based local pair natural orbital coupled-cluster method

For the accurate computation of relative energies, domain-based local pair natural orbital coupled-cluster [DLPNO-CCSD(T-0)] has become increasingly popular. Even though DLPNO-CCSD(T-0) shows a formally linear scaling of the computational effort with the system size, accurate predictions of relative energies remain costly. Therefore, multi-level approaches are attractive that focus the available computational resources on a minor part of the molecular system, e.g., a reaction center, where changes in the correlation energy are expected to be the largest. We present a pair-selected multi-level DLPNO-CCSD(T-0) ansatz that automatically partitions the orbital pairs according to their contribution to the overall correlation energy change in a chemical reaction. To this end, the localized orbitals are mapped between structures in the reaction; all pair energies are approximated through computationally efficient semi-canonical second-order Moller-Plesser perturbation theory, and the orbital pairs for which the pair energies change significantly are identified. This multi-level approach is significantly more robust than our previously suggested, orbital selection-based multi-level DLPNO-CCSD(T-0) ansatz [M. Bensberg and J. Neugebauer, J. Chem. Phys. 155, 224102 (2021)] for reactions showing only small changes in the occupied orbitals. At the same time, it is even more efficient without added input complexity or accuracy loss compared to the full DLPNO-CCSD(T-0) calculation. We demonstrate the accuracy of the multi-level approach for a total of 128 chemical reactions and potential energy curves of weakly interacting complexes from the S66x8 benchmark set. Published under an exclusive license by AIP Publishing.

Automated summary

In this study, a pair-selected multi-level DLPNO-CCSD (T-0) approach is proposed to accurately calculate the changes in correlation energy between orbital pairs in chemical reactions. This method is more robust than previous approaches and more efficient without adding complexity or sacrificing accuracy.