Predictive stochastic analysis of massive filter-based electrochemical reaction networks

Abstract

Chemical reaction networks (CRNs) are powerful tools for obtaining insight into complex reactive processes. However, they are difficult to employ in domains such as electrochemistry where reaction mechanisms and outcomes are not well understood. To overcome these limitations, we report new methods to assist in CRN construction and analysis. Beginning with a known set of potentially relevant species, we enumerate and then filter all stoichiometrically valid reactions, constructing CRNs without reaction templates. By applying efficient stochastic algorithms, we can then 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 contribute to SEI formation. This methodology enables the exploration of vast chemical spaces, with the potential for applications throughout electrochemistry.