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3754947
New Computational Tools for Modeling Reactions at Battery Electrodes.
Date
August 24, 2022
Contemporary Li-ion battery technology is only possible because of the adventitious formation of electrode passivation layers (the solid-electrolyte interphase, SEI) from electrolyte degradation products. After decades of research, gaps persist in our molecular scale understanding of the formation of these layers and how they respond to changes in electrolyte chemistry. Molecular simulation has played a critical role in rationalizing observations and resolving many of the individual steps for specific systems, but there are several formidable gaps for performing authentically predictive simulations of SEI formation. One is that modeling electron dynamics of large systems—as occurs during SEI formation—is notoriously expensive. A second is that electrode interfaces are heterogeneous and require large system sizes to model. For these reasons, direct modeling of electron dynamics during SEI formation is likely to remain impossible until a qualitative shift occurs in our ability to approximate quantum dynamics. Nevertheless, we propose a “kinetics without dynamics” approach to simulating these interfaces that uses transition state theory to describe the reaction kinetics and non-reactive molecular dynamics to describe the nuclear dynamics of the electrodeposition process. The basic idea is that the most pressing questions about SEI formation and breakdown concern the kinetic competition of distinct pathways rather than the character of the transition states (TS), which are sufficiently approximated by calculations on smaller model systems. In this presentation I’ll provide a progress report on our implementation of this simulation methodology and its application to Li-ion electrolytes. The main deliverables of this approach are the simulation of electrodeposition with a real time coordinate at the cost of classical molecular dynamics. The long-term prospects of applying this method to SEI breakdown processes and the means of generating robust reaction networks as inputs for the method will also be discussed.