A Solution‐Mediated Pathway for the Growth of the Solid Electrolyte Interphase in Lithium‐Ion Batteries

Abstract

Lithium‐ion batteries (LIBs) are a widely used battery technology. During the initial LIB cycle, a passivation layer, called the solid electrolyte interphase (SEI), forms on the anode surface, which plays a crucial role in the performance and long‐term cyclability of LIBs. The overall mesoscale mechanisms of SEI formation and its composition remain elusive both in experimental and computational approaches. Here a multiscale approach to comprehensively characterize the growth and composition of the SEI based on a chemistry‐specific reaction network is presented. Generating an ensemble of over 50 000 simulations representing different reaction conditions, it is found that the organic SEI forms and grows in a solution‐mediated pathway by aggregation of SEI precursors far away from the surface via a nucleation process. The subsequent rapid growth of these nuclei leads to the formation of a porous layer that eventually covers the surface. This finding offers a solution to the paradoxical situation that SEI constituents can form only near the surface, where electrons are available, but does not stop growing when this narrow region is covered. The study is able to identify the key reaction parameters that determine SEI thickness, which pave the way to optimize battery performance and lifetime.

Data‐Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

“…2023. [ 13 ] This KMC formulation follows a rejection‐free algorithm called the BKL algorithm. [ 27 ] This new formulation of KMC simulations developed by Esmaeilpour et al.…”

“…The KMC algorithm simulates SEI growth on a mesoscopic scale. [ 13 ] The SEI formation is governed by a series of reactions that start with electron reduction to generate inorganic or organic components, followed by an aggregation of inorganic components to form the inorganic part of the SEI and diffusion of organic components to form SEI cluster or organic part of the SEI. The reaction rates used to generate the SEI configurations are sampled using Latin hypercube sampling by randomly selecting the initial reaction rate set adopted from published literature [ 28‐30 ] on the rates of SEI growth.…”

“…The Kinetic Monte Carlo (KMC) protocol uses reaction rates obtained from quantitative chemical calculations to determine the magnitude of the SEI in a mesoscopic model with molecular resolution. [ 13 ] The above‐described proposed model includes spatial and temporal information about the evolving SEI, governed by a series of chemical reactions, diffusion, and aggregation mechanisms, with kinetic data obtained for specific electrolyte‐anode reactions. The process for synthesizing such an SEI configuration will be discussed in detail in the following sections.…”

Impact of electrolyte impurities and SEI composition on battery safety

“…15 The model contained a Newman-type cell model extended with an SEI. In a recent study Esmaeilpour et al 16 used a kMC approach with over 50 000 simulation conditions to find that the most probable way of SEI formation is a sequence of formation, dissolution and finally, aggregation. Furthermore, approaches using kMC methods in combination with computational chemistry support long-time existing hypotheses about the composition and morphology of the SEI.…”

Caracal: A Versatile Ring Polymer Molecular Dynamics Simulation Package

“…The quantitative description of reactive processes constitutes a central part of computational chemistry. Chemical reaction rates k ( T ) can be used as tool to coarse-grain the dynamics of elementary reactions, which then can be fit into schemes like kinetic Monte Carlo (KMC) to simulate complex reaction networks over long time scales. − Over the years, two philosophies of calculating k ( T ) values have emerged . First, full-dimensional wave packet scattering calculations can be done.…”