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Related Experiment Videos

Coupling Real-Time Time-Dependent Density Functional Theory with Polarizable Force Field.

Greta Donati1, Andrew Wildman1, Stefano Caprasecca2

  • 1Department of Chemistry, University of Washington , Seattle, Washington 98195, United States.

The Journal of Physical Chemistry Letters
|October 11, 2017
PubMed
Summary
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Real-time time-dependent density functional theory (RT-TDDFT) calculations are now feasible for large systems. A new polarizable QM/MMPol model accurately captures mutual polarization effects for extended systems.

Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Spectroscopy

Background:

  • Real-time time-dependent density functional theory (RT-TDDFT) is crucial for understanding time-dependent properties and spectroscopic observables.
  • Current computational limitations restrict RT-TDDFT to smaller systems.
  • Mixed quantum mechanical and molecular mechanical (QM/MMPol) models offer efficient approximations for large systems.

Purpose of the Study:

  • To develop and validate a novel coupling scheme between induced dipole-based QM/MMPol and RT-TDDFT.
  • To enable accurate RT-TDDFT calculations on extended systems.
  • To account for polarization effects between QM and MM regions.

Main Methods:

  • Development of a coupling scheme integrating induced dipole QM/MMPol with RT-TDDFT.

Related Experiment Videos

  • Validation of the model by comparing calculated spectra against established RT-TDDFT and linear-response TDDFT methods.
  • Application to extended systems requiring accurate treatment of mutual polarization.

    • Main Results:

    • The developed QM/MMPol-RT-TDDFT approach provides accurate spectroscopic observables.
    • The model successfully accounts for mutual polarization between QM and MM regions.
    • Computational feasibility of RT-TDDFT for larger, extended systems is demonstrated.

    Conclusions:

    • The novel QM/MMPol-RT-TDDFT method offers an accurate and efficient approach for studying extended systems.
    • This advancement significantly expands the applicability of RT-TDDFT in computational chemistry.
    • The model facilitates a deeper understanding of complex, time-dependent phenomena in large molecular systems.