We present a new approach to construct and explore CRNs in electrochemistry that is capable of extracting unique insights and generating hypotheses to guide further in-depth analysis. First, we describe our method of High-Performance Reaction Generation (HiPRGen). Beginning with a set of possible species that could contribute to the chemistry of interest (S init ), HiPRGen enumerates all stoichiometrically valid reactions and employs user-defined filters to eliminate reactions based on physical or practical criteria while aiming to retain a diverse and chemically reasonable set R. To overcome the scaling limitations of graphbased pathfinding, we explore CRNs with a stochastic approach, sampling the reactive space based without knowledge of reaction kinetics. We can then extract paths to any molecule formed in the trajectories and heuristically identify the products of the network as a function of initial conditions. The combination of HiPRGen with stochastic network analysis allows for the investigation of electrochemical reactivity without prior knowledge of reaction mechanisms or end products for the first time. We demonstrate and validate this approach with an application to SEI formation. We first identify 36 products of a HiPRGen-constructed network of roughly 5,200 molecules and 86,000,000 reactions by analyzing the average of many stochastic simulations. The identified network products include many species reported in the SEI literature as well as a range of unreported species. To demonstrate the plausibility of these network products and their associated formation pathways, we use first-principles calculations to refine the shortest thermodynamic paths to two previously unreported products, discovering chemically plausible elementary mechanisms. Further bespoke calculations indicate that several previously-unreported network products (or related intermediates) could reasonably emerge during SEI formation and could contribute to the production of other, experimentally-identified SEI products. The methods described here serve as a starting point for predictive studies of reactivity in electrochemistry where existing knowledge is limited.