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Neil Shenvi

Publications and source records attributed to Neil Shenvi.

2 recordsLinked to original sources

Vibrational relaxation of NO on Au(111) via electron-hole pair generation.

Recent experiments have demonstrated the breakdown of the Born-Oppenheimer approximation when NO undergoes inelastic scattering from a Au(111) surface. In this paper, we provide a simple theoretical model for understanding this phenomenon. Our model predicts multiquanta vibrational relaxation through the creation of high-energy electron-hole pair excitations in the metal. Using experimentally determined parameters, our model gives qualitatively accurate predictions for the final vibrational state populations of the scattered molecule and predicts efficient conversion of vibrational energy into electronic energy.

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Efficient chemical kinetic modeling through neural network maps.

An approach to modeling nonlinear chemical kinetics using neural networks is introduced. It is found that neural networks based on a simple multivariate polynomial architecture are useful in approximating a wide variety of chemical kinetic systems. The accuracy and efficiency of these ridge polynomial networks (RPNs) are demonstrated by modeling the kinetics of H(2) bromination, formaldehyde oxidation, and H(2)+O(2) combustion. RPN kinetic modeling has a broad range of applications, including kinetic parameter inversion, simulation of reactor dynamics, and atmospheric modeling.

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