Our group has introduced a “Fuel property first” design approach to reduce emissions and increase performance. 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 a quantitative structure-property relationship (QSPR) model for sooting tendency based on the experimental yield sooting index (YSI), developed by collaborators at Yale (Prof. L. Pfefferle and Dr. C. McEnally). This is the first fuel property predictive tool using ML (Machine Learning) approaches in combustion research. We have started to build kinetic mechanisms of soot precursor formation during combustion using DFT and flow reactor experiments (collaboration with Dr. R. McCormick, NREL) to show how the fundamental chemistry affects this practical engineering problem..