Theoretical prediction of hydrogen bond basicity

Literature Information

Publication Date 2000-06-26
DOI 10.1039/B003026K
Impact Factor 3.676
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Abstract

Ab initio and DFT calculations on around 50 hydrogen bond, or Lewis bases and their complexes with hydrogen fluoride are reported. A range of calculated properties of both free bases and complexes are correlated with β, an experimental scale of hydrogen bond basicity. For the entire range of bases, we find that none of the isolated base properties correlate well with β, although some family-dependent models can be constructed for O and N bases separately. In contrast, several properties of the HF complexes and changes on complexation correlate strongly with β, including the H-bond binding energy, changes in the electron density at bond critical points, and the lengthening and weakening of the H–F bond on H-bond formation. Using these models, new values of β are predicted for molecules and functional groups which have no experimentally measured counterparts, including carbenes, C-ylides, phosphines, and amine and phosphine oxides.

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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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