Traversing Dense Networks of Elementary Chemical Reactions to Predict Minimum‐Energy Reaction Mechanisms. Issue 4 (17th December 2019)

• Record Type: Journal Article
• Authors: Robertson, Christopher; Ismail, Idil; Habershon, Scott
• Is Part Of: ChemSystemsChem. Volume 2:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-17
• Subjects: Catalysis -- graph searching algorithms -- nudge-elastic-band -- reaction discovery -- reaction mechanism; Synthetic biology -- Periodicals; Artificial cells -- Periodicals; Chemical systems -- Periodicals; Biochemistry -- Periodicals; Biotechnology -- Periodicals; 572
• DOI: 10.1002/syst.201900047
• Languages: English
• ISSNs: 2570-4206

Abstract

Abstract: Numerous different algorithms have been developed over the last few years which are capable of generating large, dense chemical reaction networks describing the inherent chemical reactivity of a collection of discrete molecules. 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. The approach is successfully tested for reaction networks (containing tens of thousands of possible reactions) describing the carbon monoxide oxidation on platinum nanoparticles. Abstract : Connect the dots : We propose a chemical reaction network analysis scheme which can be used to extract sets of mechanisms connecting well‐defined reactant and product species. Our algorithm is presented in detail, before being demonstrated in an Initial application to CO oxidation on platinum nanoparticles.