Predictive stochastic analysis of massive filter-based electrochemical reaction networks

12 January 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

Reaction networks
Chemical reaction networks
Electrochemistry
Solid electrolyte interphase
Lithium-ion batteries
Electrolytes
Monte Carlo
Thermodynamics
Chemical kinetics
Reaction mechanisms
Reaction discovery
Stochastic pathfinding
Reductive degradation

Supplementary materials

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Supplementary information
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Supplementary information
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Reaction pathways
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All data used to construct mechanisms (molecular structures, thermodynamics, vibrational frequencies, and frequency modes) provided in the Javascript Object Notation (JSON) format
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