Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is a challenge for modern computational protocols. In order to remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed to provide mechanistic analysis in catalytic systems. The primary advantage of the presented three-step approach over the existing automated pathways generation methods is the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvent). It is not limited to pre-defined chemical rules, does not require pre-alignment of reaction mixture components consistent with a reaction coordinate and is not agnostic to the chemical nature of transformations. Conformer exploration, Reactive event identification and Reaction network analysis are the main steps taken for understanding the underlying mechanistic pathways in catalytic mixtures given the reaction mixture as the input. Such a methodology allows to comprehensively explore the catalytic systems in realistic conditions for either previously observed or completely unknown reactive events. The expert bias is sought to be removed in either of the steps and chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.
Molecular graph theory in reaction identification Relevant Molecular Graph Theory Terminology