When PES exploration can be used to generate CRNs, new intermediate and product species can be identified automatically via reactions starting from known species. Likewise, when templates can be used, new species are generated by repeatedly applying reaction motifs to provided or previously generated reactants. As we have explained, neither PES exploration nor templates can presently be applied to electrochemistry. Instead, HiPRGen currently requires an input set of species containing all molecules to be included in the CRN and their calculated properties. For applications to Li-ion SEI formation, where the LIBE dataset of molecular thermochemistry already exists, this is not a significant limitation. More generally, the reliance on a precomputed dataset of species is problematic, as it potentially creates a high computational barrier to entry for the user and limits predictive discovery of novel species and pathways they participate in. We are actively working to overcome this requirement, developing a framework to automatically generate CRNs from only a set of known starting molecules by leveraging calculated network products to strategically guide iterative network expansion and machine learning (ML) to reduce the number of required high-throughput DFT calculations.