Sticking of Methane on Palladium Oxide: a Computational Approach
Examensarbete för masterexamen
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|Type: ||Examensarbete för masterexamen|
|Title: ||Sticking of Methane on Palladium Oxide: a Computational Approach|
|Authors: ||Svensson, Rasmus|
|Abstract: ||The catalytic properties of palladium oxide for the combustion of methane have
been studied extensively in recent years. The rate-determining step of this reaction
is believed to be the dissociation of methane on the surface. The rate of the event
is dependent on both the active sites of the catalyst and the energy and orientation
of the incoming methane molecules. The dependence of energy and orientation is
often summarized in a sticking coefficient.
Here, we will address the challenge of calculating the sticking coefficient from firstprinciples.
However, due to the large number of trials and large time scales required
to study this event, ab initio molecular dynamics would be too computationally
expensive to perform, and an alternative approach using neural networks is applied.
The adsorption position on the active sites and the activation energy of the
dissociation process are studied using density functional theory. To determine the
probability of a sticking event, a neural network is trained to predict the multidimensional
potential energy surface, which is used to perform molecular dynamics.
The density functional theory calculations confirm that the active sites of the catalyst
are the under-coordinated palladium atoms, with an apparent activation energy
of 0.2 eV for the dissociation reaction. The neural network is able to predict the
energies of the system five orders of magnitude faster than regular density functional
theory calculations, with an MAE of 0.02 eV. The molecular dynamics suggest that
the previously believed most probable transition path might be dominated by the
sum of the other, less likely, transition paths. The hope is that the results and
understanding obtained from this computational study can be used to assist in the
discovery of more efficiently designed catalysts in the future.|
|Keywords: ||Density functional theory;neural networks;methane;palladium oxide;sticking;adsorption;dissociation;molecular dynamics;potential energy surface;catalysis;activation energy|
|Issue Date: ||2021|
|Publisher: ||Chalmers tekniska högskola / Institutionen för fysik|
|Collection:||Examensarbeten för masterexamen // Master Theses|
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