Kohn-Sham Decomposition in Real-Time Time-Dependent Density-Functional Theory : An Efficient Tool for Analyzing Plasmonic Excitations
Rossi, T. P., Kuisma, M., Puska, M. J., Nieminen, R. M., & Erhart, P. (2017). Kohn-Sham Decomposition in Real-Time Time-Dependent Density-Functional Theory : An Efficient Tool for Analyzing Plasmonic Excitations. Journal of Chemical Theory and Computation, 13(10), 4779-4790. https://doi.org/10.1021/acs.jctc.7b00589
Published inJournal of Chemical Theory and Computation
© 2017 American Chemical Society. This is a final draft version of an article whose final and definitive form has been published by ACS. Published in this repository with the kind permission of the publisher.
Electronic excitations can be efficiently analyzed in terms of the underlying Kohn-Sham (KS) electron-hole transitions. While such a decomposition is readily available in the linear-response time-dependent density-functional theory (TDDFT) approaches based on the Casida equations, a comparable analysis is less commonly conducted within the real-time-propagation TDDFT (RT-TDDFT). To improve this situation, we present here an implementation of a KS decomposition tool within the local-basis-set RT-TDDFT code in the free GPAW package. Our implementation is based on postprocessing of data that is readily available during time propagation, which is important for retaining the efficiency of the underlying RT-TDDFT to large systems. After benchmarking our implementation on small benzene derivatives by explicitly reconstructing the Casida eigenvectors from RT-TDDFT, we demonstrate the performance of the method by analyzing the plasmon resonances of icosahedral silver nanoparticles up to Ag561. The method provides a clear description of the splitting of the plasmon in small nanoparticles due to individual single-electron transitions as well as the formation of a distinct d-electron-screened plasmon resonance in larger nanoparticles. ...
PublisherAmerican Chemical Society
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Related funder(s)Academy of Finland
Funding program(s)Postdoctoral Researcher, AoF
Additional information about fundingWe thank the Academy of Finland for support through its Centres of Excellence Programme (2012−2017) under Projects No. 251748 and No. 284621. M.K. is grateful for Academy of Finland Postdoctoral Researcher funding under Project No. 295602. T.P.R. thanks the Vilho, Yrjö and Kalle Vaïsalä̈ Foundation of the Finnish Academy of Science and Letters and the Finnish Cultural Foundation for support. We also thank the Swedish Research Council, the Knut and Alice Wallenberg Foundation, and the Swedish Foundation for Strategic Research for support. We acknowledge computational resources provided by CSC − IT Center for Science (Finland), the Aalto Science-IT project (Aalto University School of Science), and the Swedish National Infrastructure for Computing at NSC (Linköping) and at PDC (Stockholm). Notes The authors declare no competing financial interest. ...
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