Application of spin-ratio scaled MP2 for the prediction of intermolecular interactions in chemical systems

Literature Information

Publication Date 2017-10-03
DOI 10.1039/C7CP04391K
Impact Factor 3.676
Authors

Samuel Y. S. Tan, Luke Wylie, Ivan Begic, Dennis Tran, Ekaterina I. Izgorodina


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Abstract

Accurate prediction of intermolecular interactions plays a pivotal role in many areas of chemistry and biology including (but not limited to) the design of pharmaceuticals, solid electrolytes and food additives. Here we present the application of the recently developed spin-ratio scaled MP2 method (termed SRS-MP2) to six different datasets covering a wide range of interaction types from strong hydrogen bonding to van der Waals dispersion and π–π stacking. The method achieves a remarkably low mean absolute error of 1.6 kJ mol−1 across all interaction types including semi-Coulombic systems such as organic ionic salts. The new SRS-MP2 method offers high level of accuracy for studying intermolecular interactions commonly found in molecular systems of chemical and biological relevance without the need for including additional terms in the formulation. This finding represents a new paradigm in the development of wavefunction-based methods for intermolecular interactions.

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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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