Someday soon, oil refineries may trade in crude oil for agricultural waste like corn stalks or renewable plants like switchgrass in order to produce sustainable biofuels. But we're not there quite yet; converting those products into usable chemicals on a large scale requires efficient catalytic reactions, which researchers are still hunting for. Recently, Conway Assistant Professor Reid Van Lehn and his colleagues in the Department of Chemical and Biological Engineering have found a way to speed up the process of finding suitable reaction conditions using machine learning, which may help the era of biofuels come a little bit sooner. One of the ways to convert lignocellulosic biomass into usable fuels is via acid-catalyzed reactions, which usually take place in water. It's often a slow process, but research has shown that the addition of certain organic cosolvents can increase reaction rates 100-fold or more.