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 interaction of hydrogen with palladium surfaces represents one of the model systems for the study of the adsorption and absorption at metal surfaces. Theoretical gas-surface dynamics studies have usually concentrated on the adsorption dynamics on clean surfaces. Only recently it has become possible, based on advances in the electronic structure codes and improvements in the computer power, to address the much more complex problem of the adsorption dynamics on precovered surfaces. Here, I present ab initio molecular dynamics (AIMD) simulations based on periodic density functional theory (DFT) calculations of the adsorption of H(2) on hydrogen-precovered Pd(100) for a broad variety of different hydrogen coverage structures. The stability of the adsorbate structures and the adsorption dynamics are analyzed in detail. Calculated sticking probabilities are larger than expected for pure site-blocking consistent with experimental results. It turns out that the adsorption dynamics on the strongly corrugated surfaces depends sensitively on the dynamic response of the substrate atoms upon the impact of the impinging H(2) molecules. In addition, for some structures the adsorption probability was evaluated as a function of the kinetic energy. Adsorbate structures corresponding to the same coverage but with different arrangements of the adsorbed atoms can lead to a qualitatively different dependence of the adsorption probability on the kinetic energy changing also the order of the preferred structures, as far as the adsorption is concerned, as a function of the kinetic energy. This indicates that dynamical effects such as steering and dynamical trapping play an important role in the adsorption on these precovered substrates.The Journal of Chemical Physics 11/2011; 135(17):174707. · 3.12 Impact Factor
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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. · 1.84 Impact Factor
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ABSTRACT: The computation time for Monte Carlo (MC) simulation of a nanostructure growth process was shown to be reduced by an order of magnitude compared to conventional atomistic and meso-scale models through the prediction of the structure evolution ahead of every growth step. This approach used to grow of one of the longest (∼194 nm) reported carbon nanotubes (CNTs) from atomistic simulations. The key to the approach is the finding from simulation experiments that the CNT synthesis process exhibits nonlinear and recurring near-stationary dynamics.Chemical Physics Letters 03/2012; 530:81–85. · 2.15 Impact Factor