Using HiPRGen, we constructed a reaction network that seeks to describe SEI formation in lithium-ion batteries. An initial set of species taken from the Lithium-Ion Battery Electrolyte (LIBE) dataset, which was designed for studies of reactivity in battery electrolytes. LIBE contains the properties of 17,190 species of various charges (-1, 0, 1) and spin multiplicities (1, 2, 3) calculated using density functional theory (DFT) at the ωB97X-V/def2-TZVPPD/SMD level of theory. These species were generated by quasi-exhaustively enumerating the sub-fragments of a set of initial electrolyte and interphase species and then selectively recombining a subset of those fragments based on valence rules and machine learning. Importantly, no knowledge of SEI formation mechanisms was used to generate LIBE. For this work, network construction began with a subset of LIBE containing all species comprised of only carbon, hydrogen, oxygen, and/or lithium. This subset, which we call LIBE-CHOLi, contains 8,904 species.