ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

Abstract

Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment 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 identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the 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 the 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.

HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts

“…The individual entities in the database are automatically provided in parallel as input to the exploration step, where the chemical space around each structure is exhaustively explored for alternative chemical structures and new reactions. The detailed description of the ReNeGate workflow is provided elsewhere . Further details on relevant molecular graph theory terminology and molecular graph theory in reaction identification are included in Section S1 of the Supporting Information.…”

“…Thus, our workflow inherits the inaccuracies derived from the description of the model system using a specific level of theory. However, we have designed our workflow to be able to analyze trajectories of arbitrary accuracy and complexity and are agnostic in our analysis to any specific level of accuracy …”

Performance of homogeneous catalysts viewed in dynamics

“… 88 The expert-bias-free computational exploration of potential deactivation channels using graph-based reaction network analysis indicates that such reactivity may open a path towards partial ligand dissociation and the formation of low-coordinate species that may represent the onset of long-term catalyst deactivation. 89 …”

“…88 The expert-bias-free computational exploration of potential deactivation channels using graph-based reaction network analysis indicates that such reactivity may open a path towards partial ligand dissociation and the formation of lowcoordinate species that may represent the onset of long-term catalyst deactivation. 89 The loss of the catalyst during the activation stage typically leads to a permanent decrease in the catalytic performance. A thorough characterization of the side products formed during the activation is typically required to spot it.…”

Multi-Time-Scale Simulation of Complex Reactive Mixtures: How Do Polyoxometalates Form?

“…The field of automated reaction network exploration has made a breakthrough over the past decade. − In line with this current paradigm, we recently developed methods to automate the discovery of CRNs in polyoxometalates . Taking into account the thermodynamics of the multispecies multiequilibria problem, our methods were successfully employed for predicting the aqueous speciation diagrams (metal concentration vs pH) at equilibrium for five families of isopolyoxometalates including W, Mo, V, Nb, and Ta. , Also, we introduced recently new tools for the graphical analysis of large CRNs and for the detection of kinetically feasible reaction pathways .…”