8:45 AM - *ES05.08.03
Approximate Density Functional Theory for Computational Heterogeneous Catalysis
Oak Ridge National Laboratory1
The theoretical prediction of new catalysts requires precise knowledge of structural information on which to base accurate quantum chemical studies such as density functional theory (DFT) investigations of activation energies and reaction energies. Often, structural information is difficult to obtain, for instance when surfaces and nanoparticles undergo structural transformation under pretreatment and reaction conditions, exposing novel reactive sites responsible for the experimentally observed catalytic activity. A theoretical study based on ideal surfaces or particles created from bulk will entirely miss these important features, resulting in a theory-experiment disconnect.
The density-functional tight-binding (DFTB) method [1-5] is an approximation to DFT that reduces the computational effort by 2-3 orders of magnitude, and was used for the prediction of the fullerene formation mechanism , the Haeckelite formation mechanism on metal surfaces , the existence of a “sweet spot” for the catalyst oxygen content of carbon nanotube formation , and many other phenomena in materials sciences requiring highly complex quantum chemical modeling. The tremendous speed of the method originates from the careful parameterization of the Hamiltonian , eliminating all expensive integral evaluations. The method can deliver accuracy in structure and energetics comparable to DFT [4,5,10], and has recently been successfully employed in the pre-screening of reaction and activation energies for heterogeneous catalysis  and in the study the topology of binary core-shell nanoparticles . In this presentation we will highlight recent accomplishments in the parameterization effort related to the DFTB-based study of heterogeneous catalysis and illustrate the predictive capabities in the context of graphene CVD synthesis on metal surfaces .
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