In addition to providing arbitrary and fixed rate coefficients, we consider only reactions with ∆G < 0 eV. This latter simplification is necessary to eliminate cycles or loops from the network. In reality, depending on temperature, reactions with ∆G above zero can occur. Furthermore, the inherent uncertainty in calculated reaction thermodynamics likely means that some number of reactions that we calculate to have ∆G slightly above zero (endergonic) in reality have ∆G slightly below zero instead (exergonic). However, the elimination of loops is practically necessary to enable CRN analysis. If all reactions have the same rate coefficient, then the presence of loops effectively ensures that any kMC simulation will be dominated by unimportant back-and-forth processes. This dramatically increases the noise in the simulations, making identification of important species and reactions difficult (see below). Beyond such practical and technical considerations, the elimination of endergonic reactions is reasonable on a physical basis within our domain of interest, electrochemistry. Electrochemical reaction cascades are often dominated by ion-and Step Fig. Methods for analyzing CRNs from stochastic simulations. a) A large number of kinetic Monte Carlo trajectories with fixed rates are calculated, beginning with the same network (defined by S f iltered and R f iltered ) and the same initial state ([x i , x j , • • • ] 0 , where x q is the quantity of species q). b) In each trajectory, the shortest reaction pathway to some species of of interest can be identified. Note that because these trajectories are stochastic, different trajectories will often yield different shortest pathways to the same product. c) To identify products of the network, a set of heuristics are applied. In order to be considered a product of the CRN, a species must be formed substantially more than it is consumed and must accumulate to a significant degree on average (that is, its average final concentration must be higher than some threshold). In addition, a product species must be reachable by some low-cost path. In the example provided, both the red and the blue species are formed significantly more than they are consumed, and both accumulate, but only the blue species can be reached by a low-cost pathway. Therefore, by this heuristic, the blue species is a network product, while the red species is not. radical-driven reactions. Such cascades should, in general, be comprised entirely or almost entirely by (often rapid) exergonic steps, meaning that the elimination of endergonic steps should not significantly affect the predictions of our simulations.