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 electrolytemagnesium 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.

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.

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 on 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 electrolyte — magnesium bistriflimide (Mg(TFSI)2) dissolved in diglyme (G2). Automated CRN analysis allows for 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 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.

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 on our ability to elucidate electrochemical reactivity, we 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 electrolyte - magnesium 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 its decomposition does not meaningfully contribute to gas evolution. The combined theoretical-experimental approach developed here provides a means to elucidate electrolyte reactivity, improving our ability to predict 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 unintuitive, 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 multiple 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.