In this article, we have described our approach to explore electrochemical reactivity using CRNs. The HiPRGen method allows for the enumeration of reactions for CRN construction in domains where reaction templates cannot be applied and where reaction mechanisms are poorly understood. The resulting networks can then be analyzed using stochastic simulations, allowing for the identification of network products and reaction pathways which can be further refined to discover elementary mechanisms. We applied this methodology to study SEI formation in Li-ion batteries, generating a network consisting of over 5,000 species and over 86,000,000 reactions. Network product prediction yielded many known SEI components as well as several species that had not previously been proposed. We identified elementary mechanisms for the formation of multiple novel network products (LFEO and bi-dioxolylidene) based on pathways obtained from CRN analysis and refined using automated DFT. CRN-derived pathways further guided the expansion of automatically obtained mechanisms, yielding additional insight. The mechanisms that we discovered validate our methods of product prediction and pathway identification and moreover indicate that LFEO could be a component of the early SEI. Our findings indicate that thermodynamic reaction pathways obtained via analysis of appropriately filtered CRNs can be used to efficiently search for transition-states of chemically meaningful reaction mechanisms, facilitating the construction of microkinetic models (an example of which we report in a separate publication). With the approach presented here, it will be possible to provide insights into a range of domains where fundamental understanding of reactivity is limited.