K4.3 PRINCIPLES OF CHEMISTRY ON THE Si(100) SURFACE FROM FIRST-PRINCIPLES CALCULATIONS, D.J. Doren, J.S. Hess, J.A. Barriocanal, A.C. Foraker, R. Konecny, Dept. of Chemistry and Biochemistry, University of Delaware, Newark, DE.
First-principles calculations have been used to study the mechanisms of a series of adsorption reactions on Si and Ge (100) surfaces [1-5]. The dissociative adsorption of water on Si(100)  and Ge(100) will be discussed as an example. Experimental evidence shows that adsorption is unactivated on Si(100), but activated on Ge(100). This subtle difference in reactivity of two such similar surfaces is reproduced in density functional theory (DFT) calculations with large basis sets, and the calculations establish a rationale for the difference.
In some reactions on semiconductor surfaces, the barrier to adsorption is greater than the energy of the lowest excited surface electronic state. In the case of hydrogen adsorption on Si(100), DFT calculations show that nonadiabatic crossings between electronic states occur at low enough energy to play a role in the reaction mechanism. Such crossings provide a plausible explanation for observed dynamical behavior .
DFT calculations have also been used to predict that some novel reactions of organic molecules can occur on Si(100) with negligible activation barriers. These reactions establish covalently-bound monolayers of organics on silicon surfaces that suggest new opportunities for surface modification. The surface analog of a common reaction from organic synthesis, a Diels-Alder reaction, will be described as a prototype [7,8]. The reaction terminates the very reactive, hydrophilic silicon surface with a hydrophobic organic layer that retains C-C double bonds, allowing for further controlled reactions. The theoretical predictions have been confirmed by several experimental groups.
Finally, a method will be described for deriving global potential energy surfaces by direct fits to results of first-principles calculations . The method uses an artificial neural network to interpolate among the results of first-principles energy calculations at several hundred points. The result is a model interaction potential that can be evaluated about 106 times faster than a first-principles calculation, yet predicts (nearly) the same energy over a wide range of coordinates. In contrast to traditional empirical potentials, no assumptions need to be made about the functional form of the potential, and accurate models can be made even for chemically reacting systems.
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