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: 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.
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ABSTRACT: A neural network (NN) approach is proposed for the representation of six-dimensional ab initio potentialenergy surfaces (PES) for the dissociation of a diatomic molecule at surfaces. We report tests of NN representations that are fitted to six-dimensional analytical PESs for H2 dissociation on the clean and the sulfur covered Pd(100) 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.Physical Review B 03/2006; 73(11). DOI:10.1103/PhysRevB.73.115431 · 3.66 Impact Factor