Rotational spectroscopy meets theory
文献情報
Cristina Puzzarini
Rotational spectroscopy is known to be a technique that is widely used to infer information on molecular structure and dynamics. In the last few decades, its role in the field of atmospheric and astrophysical investigations has rapidly grown. However, several are the challenging aspects in rotational spectroscopy, since the detection and analysis of spectra as well as interpretation of obtained results are not at all straightforward. Quantum chemistry has reached such an accuracy that can be used to disentangle these challenging situations by guiding the experimental investigation, assisting in the determination of the spectroscopic parameters, and extracting information of chemical interest. This perspective provides an overview of the theoretical background and computational requirements needed for the accurate evaluation of the spectroscopic parameters of relevance to rotational spectroscopy. The role of theory in guiding and supporting experiment is detailed through a few examples, and the interplay of experiment and theory is discussed in terms of the information of physical and chemical interest that can be derived.
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Physical Chemistry Chemical Physics

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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