An additional improvement could be made by incorporating reaction kinetics into CRN generation and stochastic analysis, as is common in the the study of CRNs. We have shown that reaction thermodynamics alone can be sufficient to predict reasonable reaction pathways as well as network products, yet a network based on only thermodynamics certainly contains many reactions that are kinetically limited and therefore not important in practice. Although there are not currently any methods capable of predicting energy barriers or rate coefficients for solution-phase electrochemical reactions without relying on expensive electronic structure methods (which cannot be applied to millions of reactions), we believe that ML could eventually provide sufficiently accurate estimates to realistically constrain network construction at minimal cost given sufficient training data.