Reaction Path Analysis Using Advanced Data Science Methods

Chemical conversion processes are controlled by the underlying reaction pathways. These paths are the foundation for reliable reaction and reactor engineering models which are invaluable in traditional design and optimization. In addition, they provide a starting basis for molecular and catalyst design. The ability to elucidate molecular and mechanistic reaction paths has been enhanced by developments in data science tools to identify of molecular reactants, products, short-lived intermediates and active surface/reactant sites to be probed. Papers using aimed at elucidating pathways, discerning the mechanism by which they occur, and advancing their utility in reaction and molecular design are invited. In particular, contributions that focus on the role of machine learning and its implications for increasing fundamental insight into reaction mechanisms and its implications for catalyst and product design are strongly encouraged. Papers from a broad range of technologically relevant areas including catalysis, hydrocarbon upgrading, combustion, hazardous waste removal, atmospheric chemistry, semiconductor material fabrication and biochemical conversion are sought.