Data for Traversing dense networks of elementary chemical reactions to predict minimum-energy reaction mechanisms

For all elementary reactions in a given reaction network, reaction rate calculations, followed by direct micro-kinetic modelling, enables one to predict macroscopic outcomes (e. g. rate laws, product selectivity) based on atomistic input data.

However, for chemical reaction networks containing thousands of reactant molecules, such simulations can be extremely time-consuming; in addition, the complex coupled time-dependence of molecular concentrations can present challenges when seeking essential mechanistic features. In this Article, we instead present an algorithm which seeks to predict the “most likely” reaction mechanism, or competing mechanisms, connecting any two user-selected reactant and product species, given a previously-generated reaction network as input.