The overarching goal of our group is to develop new methods to extract sustainable fuels, polymers and chemicals from plants. Our approach has been to develop and apply computational tools to both biological and chemical conversion processes as part of an iterative ‘model-validate-predict’ design process for de novo catalysts.

With its high carbon and hydrogen content, lignocellulosic biomass presents an alternative to petroleum as a nearly carbon-neutral precursor to upgraded liquid fuels. I will present some representative results in designing new catalysts for biological and chemocatalytic processes of biomass.

Our group has introduced a “Fuel property first” design approach to reduce emissions and increase performance for biofuel candidates. Traditional approaches for developing these mechanistic models require many years for each new molecule, a pace that is poorly suited to the large-scale search for new bioderived blendstocks. We have developed chemistry and physics informed graph neural networks models to predict few fuel properties including yield sooting index (YSI), cetane number (CN), critical temperature (T_c), and heat of vaporization (HoV). These properties are key factors in determining fuel performance, emissions, and safety, and can vary substantially between bioblendstock candidates under consideration. The model and methodology used in this work can be applied to other fuel properties, leading to rational principles for designing high-performance fuels.