Using non-empirically tuned range-separated functionals with simulated emission bands to model fluorescence lifetimes

Literature Information

Publication Date 2017-07-27
DOI 10.1039/C7CP03418K
Impact Factor 3.676
Authors

W. Y. Fan, T. S. Chwee


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Abstract

Fluorescence lifetimes were evaluated using TD-DFT under different approximations for the emitting molecule and various exchange–correlation functionals, such as B3LYP, BMK, CAM-B3LYP, LC-BLYP, M06, M06-2X, M11, PBE0, ωB97, ωB97X, LC-BLYP*, and ωB97X* where the range-separation parameters in the last two functionals were tuned in a non-empirical fashion. Changes in the optimised molecular geometries between the ground and electronically excited states were found to affect the quality of the calculated lifetimes significantly, while the inclusion of vibronic features led to further improvements over the assumption of a vertical electronic transition. The LC-BLYP* functional was found to return the most accurate fluorescence lifetimes with unsigned errors that are mostly within 1.5 ns of experimental values.

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Contents list

2021-07-17 Front/Back Matter

DOI: 10.1039/D1PY90095A

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
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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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