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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 previouslygenerated 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.

The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions and decomposition of 3-hydroperoxypropanal. We find that the kinetic modelling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable, and when one should take more care in applications to kinetics modelling.

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.