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  • Christian SpickermannEmail author
Chapter
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Part of the Springer Theses book series (Springer Theses)

Abstract

As already pointed out in the introduction, the major objective of this thesis is the investigation of methods and approaches for the calculation of the entropy on the basis of well-established quantum mechanical models such as Kohn–Sham density functional theory and Møller–Plesset perturbation theory, i.e., on the basis of the first principles of quantum mechanics. In contrast to “mechanical” properties like e.g. the dipole moment, the entropy is a thermodynamic property and therefore not in direct reach of the methods routinely applied in quantum chemistry.

Keywords

Cluster Structure Hydrogen Fluoride Plesset Perturbation Theory Phase Transition Property Empirical Force Field 
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.

References

  1. 1.
    Ahlrichs R, Bär M, Häser M, Horn H, Kölmel C (1989) Chem Phys Lett 162:165–169CrossRefGoogle Scholar
  2. 2.
    Neugebauer J, Reiher M, Kind C, Hess BA (2002) J Comput Chem 23:895-910CrossRefGoogle Scholar
  3. 3.
    Frisch MJ et al (2004) Gaussian03 Google Scholar
  4. 4.
    Zhou HX, Gilson MK (2009) Chem Rev 109:4092–4107CrossRefGoogle Scholar
  5. 5.
    Siebert X, Amzel LM Proteins (2004) 54:104–115 CrossRefGoogle Scholar
  6. 6.
    Okabe A (2000) Spatial tesselations: concepts and applications of Voronoi diagrams. Wiley, New YorkGoogle Scholar
  7. 7.
    Schröder C, Neumayr G, Steinhauser O (2009) J Chem Phys 130:194503CrossRefGoogle Scholar
  8. 8.
    Roy TK, Prasad MD (2009) J Chem Phys 131:114102CrossRefGoogle Scholar
  9. 9.
    Wilson EB Jr (1959) Adv Chem Phys 2:367–393CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Chair II of Inorganic ChemistryRuhr-University Bochum, Organometallics and MaterialsBochumGermany

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