Does basis set superposition error significantly affect post-CCSD(T) corrections?
Vladimir Fishman, Emmanouil Semidalas, Margarita Shepelenko and, Jan M. L. Martin

TL;DR
This study evaluates the impact of basis set superposition error on post-CCSD(T) corrections across different benchmarks, finding it generally negligible, especially with larger basis sets and extrapolation techniques.
Contribution
It provides a comprehensive analysis showing that BSSE has minimal effect on post-CCSD(T) corrections in thermochemistry and noncovalent interactions, clarifying its significance in computational chemistry.
Findings
Counterpoise corrections to post-CCSD(T) are much less significant than to CCSD(T) interaction energies.
BSSE effects on connected quadruple substitutions are negligible.
Extrapolation methods effectively reduce BSSE impact on atomization energies.
Abstract
We have investigated the title question for both a subset of the W4-11 total atomization energies benchmark, and for the A24x8 noncovalent interactions benchmark. Overall, counterpoise corrections to post-CCSD(T) contributions are about two orders of magnitude less important than those to the CCSD(T) interaction energy. Counterpoise corrections for connected quadruple substitutions (Q) are negligible, and or especially so. In contrast, for atomization energies, the counterpoise correction can reach about 0.05 \kcalmol~for small basis sets like cc-pVDZ, thought it rapidly tapers off with cc-pVTZ and especially aug-cc-pVTZ basis sets. It is reduced to insignificance by the extrapolation of applied in both W4 and HEAT thermochemistry protocols. In noncovalent dimers, the differential BSSE on post-CCSD(T) correlation contributions is…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Machine Learning in Healthcare
