Template-Free Reaction Networks Enable Predictive and Automated Analysis of Complex Electrochemical Reaction Cascades

Abstract

Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, existing techniques rely heavily on chemical intuition and prior knowledge, limiting their applicability in domains like electrochemistry where reaction mechanisms or stable products are not well understood and where potential energy surface exploration is computationally intractable. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters, rather than templates, we preserve species and reactions that are unintuitive but fundamentally reasonable for processes driven by an applied potential. The resulting massive CRNs can then be interrogating via stochastic methods, revealing thermodynamically bounded reaction pathways to species of interest and automatically identifying network products. We apply this methodology to study solid electrolyte interphase (SEI) formation in Li-ion batteries. Our methods automatically recover SEI products from the literature and predict previously unknown species; the predicted formation mechanisms to select products are then validated using first-principles calculations. This methodology enables the efficient de novo exploration of vast chemical spaces, with the potential for diverse applications across electrochemistry and photochemistry.