Complex Chemical Reaction Networks
Our research in complex reaction networks primarily focuses on the use of MD simulation to predict new reaction pathways when the chemistry is too complicated to enumerate by hand and/or there is insufficient knowledge to apply automated reaction mechanism generation tools. We use a variety of approaches to characterize reaction networks including statistical learning, enhanced sampling and ab initio methods. Current areas of interest include applications in combustion, pyrolysis and reactions in condensed phase.