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Density Functional Theory and Car-Parrinello Molecular Dynamics Methods

  • Francesco Buda
Part of the Advances in Photosynthesis and Respiration book series (AIPH, volume 26)

The recent progress in fi rst-principles electronic structure calculations together with the steadily increase in computational power have considerably broadened the range and scope of application of these theoretical methods. Of particular interest is the combination of density functional theory calculations with classical molecular dynamics simulations, the so-called Car-Parrinello method. After twenty years since the publication of Car and Parrinello’s seminal paper, this method has been proven to be a powerful tool for studying a large variety of problems, from condensed matter physics, to chemistry and more recently to highly complex systems of biophysics and biochemistry. This success is also linked to the considerable progress achieved in the development of more accurate approximations of the exchange-correlation functional in the density functional theory which are approaching the required chemical accuracy. The calculation of excitation energies and excited states has also become more manageable within the time-dependent density functional scheme developed in the last two decades. This development has paved the way for the study of photoactivated processes and photochemical reactions. Hybrid schemes combining quantum-mechanics with classical molecular mechanics (QM/MM) are being also progressively improved and provide an excellent framework to study quite extended and complex biological systems with a high degree of accuracy. In this chapter, we introduce the most important concepts of these methods and describe the advancements that are relevant for the application of these methodologies to photo-biological processes and photosynthesis.

Keywords

Density Functional Theory Chem Phys Local Density Approximation Nuclear Magnetic Resonance Chemical Shift Photoactive Yellow Protein 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V 2008

Authors and Affiliations

  • Francesco Buda
    • 1
  1. 1.Leiden Institute of ChemistryLeiden UniversityNetherlands

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