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Vol. 28 No. 3
May-June 2006

Making an imPACt | Recent IUPAC technical reports and recommendations that affect the many fields of pure and applied chemistry.
See also www.iupac.org/publications/pac

How to Access Structure and Dynamics of Solutions: The Capabilities of Computational Methods (Special Topic Article)

Bernd M. Rode and Thomas S. Hofer
Pure and Applied Chemistry
Vol. 78, No. 3, pp. 525–539 (2006)
doi:10.1351/pac200678030525

Every experimental result is only as good as the theoretical model employed for its interpretation. Usually there is a complicated way from the actually measured data to the final results; for example, the determination of a structure: A theoretical model has to be defined, to which the measured data are fitted until the "best possible" agreement is achieved, mostly within a few percent of deviation. Though not too error-prone in the case of highly regular solids, this procedure becomes more difficult with gases (with their high mobility of components) and with liquids (where high mobility is combined with a density similar to solids). Any a priori postulated models can be much too simplified.

One of the ways in which simulations are superior to experiments is that they offer the possibility of easily evaluating any kind of atom–atom pair distribution. In more complex systems (e.g., mixed solvents and solutions simultaneously containing several solute species), this is an enormous advantage over spectroscopic approaches, where only averaged data (e.g., atom-atom distances) can be "seen." The example shown in this figure illustrates the overlay of various atom–atom radial distribution functions for Ca(II) ion in aqueous ammonia [from A. Tongraar, K. Sagarik, B.M. Rode. Phys.Chem. 4, 628 (2002)].

The quality of theoretical models plays a pivotal role in the determination of structural parameters, and even more, when other physicochemical phenomena such as reaction dynamics and mechanisms (where all interpretation of measurements depends on a correct structural model plus corresponding mechanistic models) are evaluated.

In this article, the progress of computational chemistry in the treatment of liquid systems is outlined. Emphasis is on the combination of the statistical methods—Monte Carlo and molecular dynamics—with quantum mechanics as the main foundation of this progress. The difficulties of experimental studies of liquid systems without having obtained sophisticated theoretical models describing the structural entities and the dynamical behavior of these liquids demonstrate that chemistry research is in a transition phase, where theory and high-performance computing have not only become a valuable supplement but an essential and almost indispensable component to secure a correct interpretation of measured data.



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