No abstract

A most basic and puzzling enigma in surface science is the description of the dissociative adsorption of O(2) at the (111) surface of Al. Already for the sticking curve alone, the disagreement between experiment and results of state-of-the-art first-principles calculations can hardly be more dramatic. In this Letter we show that this is caused by hitherto unaccounted spin selection rules, which give rise to a highly nonadiabatic behavior in the O(2)/Al(111) interaction. We also discuss problems caused by the insufficient accuracy of present-day exchange-correlation functionals.

The accurate description of molecule-surface interactions requires a detailed knowledge of the underlying potential-energy surface (PES). Recently, neural networks (NNs) have been shown to be an efficient technique to accurately interpolate the PES information provided for a set of molecular configurations, e.g., by first-principles calculations. Here, we further develop this approach by building the NN on a new type of symmetry functions, which allows to take the symmetry of the surface exactly into account. The accuracy and efficiency of such symmetry-adapted NNs is illustrated by the application to a six-dimensional PES describing the interaction of oxygen molecules with the Al(111) surface.

A neural network ͑NN͒ approach is proposed for the representation of six-dimensional ab initio potentialenergy surfaces ͑PES͒ for the dissociation of a diatomic molecule at surfaces. We report tests of NN representations that are fitted to six-dimensional analytical PESs for H 2 dissociation on the clean and the sulfur covered Pd͑100͒ surfaces. For the present study we use high-dimensional analytical PESs as the basis for the NN training, as this enables us to investigate the influence of phase space sampling on adsorption rates in great detail. We note, however, that these analytical PESs were obtained from detailed density functional theory calculations. When information about the PES is collected only from a few high-symmetric adsorption sites, we find that the obtained adsorption probabilities are not reliable. Thus, intermediate configurations need to be considered as well. However, it is not necessary to map out complete elbow plots above nonsymmetric sites. Our study suggests that only a few additional energies need to be considered in the region of activated systems where the molecular bond breaks. With this understanding, the required number of NN training energies for obtaining a high-quality PES that provides a reliable description of the dissociation and adsorption dynamics is orders of magnitude smaller than the number of total-energy calculations needed in traditional ab initio on the fly molecular dynamics. Our analysis also demonstrates the importance of a reliable, high-dimensional PES to describe reaction rates for dissociative adsorption of molecules at surfaces.

We present a unified approach for linear and nonlinear sensitivity analysis for models of reaction kinetics that are stated in terms of systems of ordinary differential equations (ODEs). The approach is based on the reformulation of the ODE problem as a density transport problem described by a Fokker-Planck equation. The resulting multidimensional partial differential equation is herein solved by extending the TRAIL algorithm originally introduced by Horenko and Weiser in the context of molecular dynamics (J. Comp. Chem. 2003, 24, 1921) and discussed it in comparison with Monte Carlo techniques. The extended TRAIL approach is fully adaptive and easily allows to study the influence of nonlinear dynamical effects. We illustrate the scheme in application to an enzyme-substrate model problem for sensitivity analysis w.r.t. to initial concentrations and parameter values.