Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and cont...

Full description

Bibliographic Details
Published in:The Journal of Chemical Physics
Main Authors: Jiang, Andy, Glick, Zachary L., Poole, David, Turney, Justin M., Sherrill, C. David, Schaefer, Henry F.
Other Authors: U.S. Department of Energy, National Science Foundation
Format: Article in Journal/Newspaper
Language:English
Published: AIP Publishing 2024
Subjects:
Online Access:http://dx.doi.org/10.1063/5.0219963
https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/5.0219963/20124231/082502_1_5.0219963.pdf
Description
Summary:We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).