Prediction of molecular-dynamics simulation results using feedforward neural networks: reaction of a C2 dimer with an activated diamond (100) surface.
ABSTRACT A new approach involving neural networks combined with molecular dynamics has been used for the determination of reaction probabilities as a function of various input parameters for the reactions associated with the chemical-vapor deposition of carbon dimers on a diamond (100) surface. The data generated by the simulations have been used to train and test neural networks. The probabilities of chemisorption, scattering, and desorption as a function of input parameters, such as rotational energy, translational energy, and direction of the incident velocity vector of the carbon dimer, have been considered. The very good agreement obtained between the predictions of neural networks and those provided by molecular dynamics and the fact that, after training the network, the determination of the interpolated probabilities as a function of various input parameters involves only the evaluation of simple analytical expressions rather than computationally intensive algorithms show that neural networks are extremely powerful tools for interpolating the probabilities and rates of chemical reactions. We also find that a neural network fits the underlying trends in the data rather than the statistical variations present in the molecular-dynamics results. Consequently, neural networks can also provide a computationally convenient means of averaging the statistical variations inherent in molecular-dynamics calculations. In the present case the application of this method is found to reduce the statistical uncertainty in the molecular-dynamics results by about a factor of 3.5.
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ABSTRACT: The adsorption of molecular hydrogen on sulfur- and chlorine-covered Pd(100) in a (2 × 2) geometry is studied by ab initio molecular dynamics simulations. The potential energy surfaces of H2/S(2 × 2)/Pd(100) and H2/Cl(2 × 2)/Pd(100) are rather similar. Consequently, also the dependence of the sticking probability on incident kinetic energy, angle of incidence and internal excitations are very close. For H2/S(2 × 2)/Pd(100), previous results obtained on an interpolated ab initio potential energy surface are confirmed, except for the dependence of the sticking probability on the initial rotational state which exhibits a surprising rotational enhancement. Discrepancies with respect to the experiment which remain are discussed. In the simulations, several subsurface penetration events have been found, preferentially close to the sulfur or chlorine adatoms, respectively. This is explained by lower barriers caused by the destabilization of hydrogen adsorption close to the repulsive adatoms.Surface Science 02/2013; 608:249–254. DOI:10.1016/j.susc.2012.10.015 · 1.87 Impact Factor
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ABSTRACT: Ab initio molecular dynamics (AIMD) simulations represent a versatile tool to study dynam-ical processes of molecules, solids and/or surfaces as will be demonstrated focusing on the adsorption dynamics of molecules on nanostructured surfaces such as precovered or stepped surfaces. Performing AIMD simulations corresponds to unbiased computer experiments allow-ing the identification of mechanistic processes whose sequence is sometimes unexpected. This will be illustrated using the H 2 adsorption on a precovered surface without any dimer vacancies as an example. In addition, first results related to the adsorption of molecular oxygen on stepped platinum surfaces will be presented.