Modeling environment effects on spectroscopies through QM/classical models

Literature Information

Publication Date 2013-01-08
DOI 10.1039/C3CP44417A
Impact Factor 3.676
Authors

Benedetta Mennucci


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Abstract

In this perspective, an overview of the recent progress in the combination of quantum mechanical (QM) simulations of spectroscopies with classical models to include environment effects is presented. Both atomistic and continuum formulations of the classical models are reviewed together with a critical analysis of their respective pros and cons. In particular, the different strategies developed within the two families of methods to include mutual polarization effects between the QM and the classical part or to properly treat the statistical sampling are presented and discussed. Examples of applications to different types of environment are also presented to show how these hybrid approaches can be used to obtain an accurate description of electronic, vibrational and magnetic spectroscopies even when nonequilibrium, heterogeneities, and/or specific and bulk effects are in play.

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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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