Multi-coefficient extrapolated density functional theory for thermochemistry and thermochemical kinetics

Literature Information

Publication Date 2004-12-10
DOI 10.1039/B416937A
Impact Factor 3.676
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Abstract

We have developed a new kind of multi-coefficient correlation method (MCCM) by empirically mixing correlated wave function methods and density functional methods. The new methods constitute a generalization of hybrid density functional theory and may be called multi-coefficient extrapolated density functional theory. Results by the new methods are compared to those obtained by G3SX, G3SX(MP3), CBS-Q and MCCM/3 for calculations of atomization energies, barrier heights, ionization potentials and electron affinities. These results show that the multi-coefficient extrapolated density functional theory is more accurate for thermochemistry and thermochemical kinetics than the pure wave function methods of comparable cost. As a byproduct of this work we optimized a new hybrid meta density functional theory called TPSS1KCIS, which has excellent performance for thermochemistry.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

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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