It is shown that the total charge density is a valid source to confirm hydrogen bonding without invoking a reference charge density. A set of criteria are proposed based on the theory of "atoms in molecules" to establish hydrogen bonding, even for multiple interactions involving C-H-O hydrogen bonds. These criteria are applied to several van der Waals complexes. Finally a bifurcated intramolecular C-H-O hydrogen bond is predicted in the anti-AIDS drug AZT, which may highlight a crucial feature of the biological activity of a whole class of anti-AIDS drugs.
A new type of hydrogen bond, called a dihydrogen bond, has recently been introduced. In this bond a hydrogen is donated to another (hydridic) hydrogen. We apply a set of criteria developed in the context of the theory of "atoms in molecules" that were previously successfully used to study conventional hydrogen bonds. This method enables one to characterize the dihydrogen bond on the basis of the electron density only. We investigated a dimer structure of BH 3 NH 3 at the ab initio level which contains two dihydrogen bonds that differ in strength. The combination of a theoretical density with our hydrogen-bonding criteria turns out to be a valuable new and independent source of information complementary to techniques such as NMR, IR, and structural crystallography.
Molecular mechanics is the tool of choice for the modeling of systems that are so large or complex that it is impractical or impossible to model them by ab initio methods. For this reason there is a need for accurate potentials that are able to quickly reproduce ab initio quality results at the fraction of the cost. The interactions within force fields are represented by a number of functions. Some interactions are well understood and can be represented by simple mathematical functions while others are not so well understood and their functional form is represented in a simplistic manner or not even known. In the last 20 years there have been the first examples of a new design ethic, where novel and contemporary methods using machine learning, in particular, artificial neural networks, have been used to find the nature of the underlying functions of a force field. Here we appraise what has been achieved over this time and what requires further improvements, while offering some insight and guidance for the development of future force fields.
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