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...
Out-of-equilibrium electrochemical reaction mechanisms
are notoriously
difficult to characterize. However, such reactions are critical for
a range of technological applications. For instance, in metal-ion
batteries, spontaneous electrolyte degradation controls electrode
passivation and battery cycle life. Here, to improve our ability
to elucidate electrochemical reactivity, we for the first time combine
computational chemical reaction network (CRN) analysis based on density
functional theory (DFT) and differential electrochemical mass spectroscopy
(DEMS) to study gas evolution from a model Mg-ion battery electrolytemagnesium
bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated
CRN analysis allows for the facile interpretation of DEMS data, revealing
H2O, C2H4, and CH3OH as
major products of G2 decomposition. These findings are further explained
by identifying elementary mechanisms using DFT. While TFSI– is reactive at Mg electrodes, we find that it does not meaningfully
contribute to gas evolution. The combined theoretical–experimental
approach developed here provides a means to effectively predict electrolyte
decomposition products and pathways when initially unknown.
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are not well understood and products are unknown. Here we report new methods of CRN generation and analysis that overcome these limitations. By constructing CRNs using filters rather than templates, we can capture species and reactions that are unintuitive but fundamentally reasonable. The resulting massive CRNs can then be interrogated 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, generating a CRN with ~86,000,000 reactions. Our methods automatically recover SEI products from the literature and predict previously unknown species. We validate their formation mechanisms using first-principles calculations, discovering novel kinetically accessible molecules. This methodology enables the de novo exploration of vast chemical spaces, with the potential for diverse applications across thermochemistry, electrochemistry, and photochemistry.
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