The ‘Feedstock to Function’ tool incorporates machine learning to predict desired properties of high-potential biobased molecules early in technology validation and certification process. Coupled with a lightweight technoeconomic and life-cycle assessment model, this tool enables users to identify promising molecules and blends for their application, overcome experimentally and kinetically derived property bottlenecks, identify cost and emissions bottlenecks, and potentially de-risk investments needed to scale up production.