In computational heterogeneous catalysis, traditionally catalytic reactions were thought to be performed on static surfaces or nanoparticles. However, operando characterization has shown that catalysts behave dynamically resulting in dynamic structural transformations under operando conditions. Nanoparticle-based catalysts, due to their limited number of atoms allow enhanced restructuring in comparison to their bulkier counterparts. Extending computational techniques to model dynamic reaction conditions would be computationally burdensome using a traditional computational method. Therefore, a method that is consistent with complex DFT results while also being less time intensive is desired to provide an in-depth mechanistic understanding for designing improved catalysts and process conditions<br/> <br/>To accomplish this, we propose the use of a semiempirical tight-binding-based approach (GFN1-xTB and GFN2-xTB) that uses a simplified quantum mechanical Hamiltonian and relies heavily on pair-wise interactions. These methods were not originally designed with dynamic catalysis in mind; therefore, reparameterization is necessary to accurately represent the structural characteristics of DFT nanoclusters. This was done by algorithmically generating random clusters containing two, three, or four platinum atoms supported on graphene sheets, using default values for the GFN1-xTB and GFN2-xTB Hamiltonian. Unique platinum clusters were isolated and refined using Quantum-Expresso DFT calculations and used to refine the Pt-C and Pt-Pt pairwise interactions of each cluster. The default and reparametrized GFN1-xTB and GFN2-xTB methods will be compared for the characterization of graphene-supported platinum clusters. Lastly, to understand the accuracy of this method we modeled and compared the absorbance of carbon monoxide (CO) on these platinum nanoclusters against experimental data.

Reparametrizing a semiempirical tight-binding-based approach for the simulation of platinum clusters on graphene

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