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A DLPNO‐CCSD(T) benchmarking study of intermolecular interactions of ionic liquids.
- Source :
-
Journal of Computational Chemistry . Jan2022, Vol. 43 Issue 2, p106-120. 15p. - Publication Year :
- 2022
-
Abstract
- The accuracy of correlation energy recovered by coupled cluster single‐, double‐, and perturbative triple‐excitations, CCSD(T), has led to the method being considered the gold standard of computational chemistry. The application of CCSD(T) has been limited to medium‐sized molecular systems due to its steep scaling with molecular size. The recent development of alternative domain‐based local pair natural orbital coupled‐cluster method, DLPNO‐CCSD(T), has significantly broadened the range of chemical systems to which CCSD(T) level calculations can be applied. Condensed systems such as ionic liquids (ILs) have a large contribution from London dispersion forces of up to 150 kJ mol−1 in large‐scale clusters. Ionic liquids show appreciable charge transfer effects that result in the increased valence orbital delocalization over the entire ionic network, raising the question whether the application of methods based on localized orbitals is reliable for these semi‐Coulombic materials. Here the performance of DLPNO‐CCSD(T) is validated for the prediction of correlation interaction energies of two data sets incorporating single‐ion pairs of protic and aprotic ILs. DLPNO‐CCSD(T) produced results within chemical accuracy with tight parameter settings and a non‐iterative treatment of triple excitations. To achieve spectroscopic accuracy of 1 kJ mol−1, especially for hydrogen‐bonded ILs and those containing halides, the DLPNO settings had to be increased by two orders of magnitude and include the iterative treatment of triple excitations, resulting in a 2.5‐fold increase in computational cost. Two new sets of parameters are put forward to produce the performance of DLPNO‐CCSD(T) within chemical and spectroscopic accuracy. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01928651
- Volume :
- 43
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Journal of Computational Chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 153749008
- Full Text :
- https://doi.org/10.1002/jcc.26776