(600cf) Machine Learning Approaches to Design Catalysts for C1 Chemistry

Simultaneous carbon dioxide and methane conversion to chemicals and fuels shows great promise in mitigating the environmental impact of greenhouse gas emissions, but requires the activation of the molecules from their energetically stable ground states and the selective cleavage of thermodynamically stable bonds.  Density functional theory calculations and micro kinetic modeling are employed to elucidate structure-property-activity relationships for carbon dioxide and methane adsorption and activation on complex oxide catalysts with controlled structure, composition, and electronic properties.  The products investigated include methanol, formic acid, formaldehyde, and CO.  Efforts to use machine learning approaches to designing superior catalysts and control process conditions will also be presented.