Predictive stochastic analysis of massive filter-based electrochemical reaction networks

Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ when reaction mechanisms and products are not thoroughly understood. Here we report new methods of CRN generation and analysis that seek to overcome these limitations. We construct CRNs by enumerating and then filtering all stoichiometrically valid reactions, avoiding the need to know reaction templates a priori. By applying efficient stochastic algorithms, we can interrogate CRNs to predict network products and reveal reaction pathways to species of interest. We apply this methodology to study solid-electrolyte interphase (SEI) formation in Li-ion batteries, automatically recovering products from the literature and predicting previously unknown species. We validate these results by combining CRN-predicted pathways with first-principles mechanistic analysis, discovering novel mechanisms which could realistically occur during SEI formation. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications throughout electrochemistry.