On the Meaning of De-Excitations in Time-Dependent Density Functional Theory Computations

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Felix Plasser
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Abstract

De-excitations play a prominent role within the mathematical formalism of time-dependent density functional theory (TDDFT) and other excited-state response methods. However, their physical meaning remains largely unexplored and poorly understood. It is the purpose of this work to shed new light on this issue. The main thesis developed here is that de-excitations are not a peculiarity of TDDFT but that they are a more fundamental property of the underlying wave functions reflecting how electrons are excited between partially occupied orbitals. The paraquinodimethane (pQDM) molecule is chosen as a convenient model system whose open-shell character can be modulated via twisting of its methylene groups. Using the one-electron transition density matrix as a rigorous basis for our analysis, we highlight qualitative and quantitative parallels in the way that de-excitations are reflected in multireference wave function and TDDFT computations. As a physically observable consequence, we highlight a lowering of the transition dipole moment that derives from destructive interference between the excitation and de-excitation contributions. In summary, we hope that this work will shed new light on formal and practical aspects regarding the application of TDDFT to excited-state computations, especially of diradicaloid systems.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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