Optical rotation calculations on large molecules using the approximate coupled cluster model CC2 and the resolution-of-the-identity approximation
文献情報
Daniel H. Friese, Christof Hättig
We investigate the performance of the approximate coupled cluster singles- and doubles model CC2 in the prediction of optical rotations of organic molecules. For this purpose we employ a combination of two test sets from the literature which include small and medium-sized rigid organic molecules and a series of helicenes. CC2 calculations on molecules as large as 11-helicene became possible through a recent implementation of frequency-dependent second-order properties for CC2 which makes use of the resolution-of-the-identity approximation for the electron repulsion integrals. The results are assessed with respect to the accuracy of the absolute values of the optical rotation and the prediction of the correct sign, which is crucial for the determination of absolute configurations. The performance of CC2 is compared with that of density functional theory at the B3LYP and CAM-B3LYP levels. Furthermore we investigated the influence of the molecular geometry and the one-electron basis set and tested to which extent spin-component scaling changes the results.
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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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