Using High-Performance Computing

to Intelligently Design Nanomaterials

Designing Stable Heterogeneous Catalysts

The deactivation of catalysts under reaction conditions is inevitable; however, great economic impacts could be realized by improving catalyst lifetime. We seek to design materials with improved resistance to deactivation through combining DFT calculations with kinetic Monte Carlo simulations in a structure-sensitive approach, focusing our efforts on catalytic ethane dehydrogenation to ethylene. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support of this research.


Selective Catalytic Conversion of Biologically-Derived Molecules

Sustainable feedstocks in energy applications and consumer products can be made more economically viable by developing frameworks for selective transformations of biologically-derived material, thereby reducing dependence on fossil fuel feedstocks. The multifunctional nature of biomass products motivates the development of computational approaches to selective transformations of these complex molecules. DFT calculations provide a fundamental understanding into the surface chemistry of various conversion reactions, enabling the design of novel catalytic materials with tailor-made surface structures and compositions toward the selective production of high-value chemical products.


Scaling and Design in Complex Catalytic Systems

Computational design using static density functional theory calculations is limited by the complexity of adsorbates and their environment. Consideration of complex systems through other tools such as ab-initio molecular dynamics comes at a very high computational cost, and therefore makes many screening and design efforts intractable. Natural scaling relationships between energies of adsorbed intermediates have been shown to enable reduction of the reaction space to functions of a handful of catalytic activity descriptors, such as adsorption energies of single atoms or simple molecules. We seek to understand the nature of scaling relationships in complex reaction environments, enabling more efficient accelerated approaches to catalyst screening in complex systems.


Computationally-Directed Design of Nanostructured Electronic Materials

We use DFT as a predictive technique for high-throughput screening of optoelectronic materials to drastically increase the speed at which novel materials are developed by eliminating the need to experimentally fabricate and characterize each composition for each structure. Key factors which are necessary for efficient charge separation, carrier mobility, chemical stability, and defect tolerance in nanostructured optoelectronic materials can be revealed by experimentally testing the results predicted by DFT calculations. Screening evaluation is made by analyzing DFT outputs such as band structures, projected density of states, dielectric function, effective masses, and bonding/antibonding orbitals. This project is a joint effort with the Panthani research group at ISU.