Spin-component scaled coupled-clusters singles and doubles optimized towards calculation of noncovalent interactions

Literature Information

Publication Date 2010-07-12
DOI 10.1039/C0CP00158A
Impact Factor 3.676
Authors

Jan Řezáč


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Abstract

The same- and opposite-spin scaling parameters for the SCS-CCSD method were reparametrized on the basis of benchmark CCSD(T)/CBS set interaction energies from the S22 set. New parameters were close to the original ones but swap between the different spin components, being 1.11 for the opposite- and 1.28 for the same-spin component. The RMSD, and especially, the largest error for the S22 were significantly reduced in comparison with the original parametrization. These statistical factors were only slightly worse when the S22x5 test set, containing not only the equilibrium but also the non-equilibrium geometries, was used. This new method, named the SCS(MI)-CCSD (“MI” stands for “Molecular Interactions”) can thus be recommended for highly accurate calculations of interaction energies of various noncovalent interaction types, for which the CCSD(T)/CBS calculations are impractical.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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