Multiscale simulation of battery degradation mechanisms presents significant challenges due to the complex interplay of processes occurring across different time and length scales. Kinetic Monte Carlo (kMC) methods have emerged as a powerful computational tool for capturing these phenomena, particularly for rare but critical events that govern battery performance and longevity. Unlike molecular dynamics (MD), which tracks atomic motions at femtosecond timesteps, kMC enables simulations of processes that occur over milliseconds to hours, making it uniquely suited for studying degradation mechanisms in battery materials.

The fundamental principle of kMC lies in its event-based approach. Rather than simulating every atomic vibration, kMC calculates transition rates between predefined states and stochastically selects which event occurs next based on these probabilities. This allows the method to effectively bypass the fast dynamics of atomic vibrations while capturing the slow evolution of materials over extended periods. For battery systems, this is particularly valuable for modeling processes like solid electrolyte interphase (SEI) growth, lithium dendrite formation, and phase transformations in electrode materials—all of which occur on timescales inaccessible to MD.

In lithium-metal anodes, dendrite growth remains a critical failure mode that kMC can effectively simulate. Dendrites form through non-uniform lithium deposition during charging, often leading to short circuits and thermal runaway. The kMC approach models this by treating lithium ion adsorption, surface diffusion, and deposition as discrete events with associated energy barriers. By parameterizing these barriers using density functional theory (DFT) calculations or experimental data, kMC simulations can predict nucleation sites, growth morphologies, and the impact of electrolyte additives on dendrite suppression. For instance, studies using kMC have shown that increasing the energy barrier for surface diffusion can lead to smoother lithium deposition, aligning with experimental observations of improved cycling stability.

SEI formation is another degradation mechanism where kMC provides unique insights. The SEI layer results from electrolyte decomposition at the electrode surface, and its growth consumes active lithium while increasing cell impedance. kMC models this process by incorporating reaction pathways for electrolyte reduction, accounting for competing reactions that produce organic and inorganic SEI components. By tracking the stochastic nature of these reactions across the electrode surface, kMC reveals how SEI heterogeneity develops over time and how it influences lithium ion transport. Simulations have demonstrated that localized high-reactivity sites can lead to uneven SEI thickness, exacerbating lithium plating inhomogeneity.

Phase transformations in electrode materials, such as lithium insertion-induced structural changes in transition metal oxides, also benefit from kMC treatment. These transformations often involve nucleation and growth processes that span multiple length scales. kMC frameworks parameterized with experimental phase diagrams and DFT-calculated migration barriers can simulate the evolution of phase boundaries and their interaction with defects like vacancies or dislocations. This capability has been applied to study lithium-rich cathodes, where phase separation impacts voltage fade and capacity loss.

Comparing kMC with MD highlights distinct advantages for battery degradation studies. While MD provides atomic-level detail of ion transport and interfacial reactions, its computational cost limits simulations to nanoseconds—far shorter than the timescales of degradation processes. MD also struggles with rare events, as the probability of observing them within feasible simulation times is low unless enhanced sampling techniques are used. In contrast, kMC naturally captures these rare events by focusing on state transitions rather than continuous dynamics. However, kMC relies on predefined reaction networks and rate constants, requiring accurate input parameters from experiments or higher-level simulations.

A key strength of kMC is its ability to bridge atomistic and mesoscopic scales. By coupling kMC with continuum models, researchers can simulate degradation across electrodes at realistic dimensions. For example, in lithium-metal batteries, atomistic kMC simulations of surface processes can inform mesoscale models of dendrite propagation, which in turn connect to cell-level performance predictions. This multiscale approach has been used to optimize electrode architectures and charging protocols to mitigate degradation.

Despite its advantages, kMC faces challenges in parameterization and computational efficiency for complex systems. Accurate rate constants are essential, and their determination often requires extensive DFT or experimental validation. Additionally, as the number of possible events grows, the computational cost of selecting the next event increases. Advanced algorithms like rejection-free kMC and parallelization strategies help address these limitations.

Recent applications of kMC in lithium-metal battery research have provided valuable insights into degradation suppression strategies. Simulations have explored the effects of artificial SEI layers, demonstrating how tailored interfaces can homogenize lithium deposition. Other studies have investigated the role of mechanical stress in dendrite growth, revealing that stress-coupled kMC models can predict morphological transitions from needle-like to mossy lithium structures. These findings guide experimental efforts to develop more stable lithium-metal anodes.

The continued development of kMC methods, including hybrid approaches that combine kMC with MD or continuum models, promises to further enhance their utility for battery degradation studies. As computational power grows and parameterization techniques improve, kMC will play an increasingly important role in understanding and mitigating battery failure mechanisms, ultimately contributing to the design of longer-lasting energy storage systems.