Kinetic Monte Carlo simulations for suppressing dendrite growth in lithium-ion batteries

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Kinetic Monte Carlo simulations for suppressing dendrite growth in lithium-ion batteries
Title:
Kinetic Monte Carlo simulations for suppressing dendrite growth in lithium-ion batteries
Journal Title:
Journal of Power Sources
Publication Date:
24 March 2026
Citation:
Lau, Y. H., Abdullah, M. F., Srinivasan, B. M., Zeng, M., Wu, G., Li, W.-Q., & Ng, M.-F. (2026). Kinetic Monte Carlo simulations for suppressing dendrite growth in lithium-ion batteries. Journal of Power Sources, 677, 239945. https://doi.org/10.1016/j.jpowsour.2026.239945
Abstract:
While widely used, Lithium-ion batteries (LIBs) have capacities that degrade from Li loss to dendrite growth. Dendrites can also connect battery electrodes, starting fires and explosions. Despite their severity, these issues remain unmitigated. To search for solutions, we run Kinetic Monte Carlo simulations of Li plating and dendrite growth at the battery anode. By doing so with different conditions, we study how such conditions affect microstructure evolution and dendrite formation. Our main finding is a threshold overpotential of 0.04V, or average current density of 0.2 mA/cm2, that should not be exceeded, to avoid dendrites and thus LIB deterioration for prolonging battery life. We established this threshold using a novel criterion we developed for classifying whether a microstructure is dendritic. To better interpret our results, we developed mean-field models that elucidate the dendrite growth mechanism and its dependence on growth conditions. We find that dendrite growth depends on the ratio of Li deposition-to-diffusion rates — the higher this ratio, the more dendritic the deposited morphology. Our results suggest the threshold for suppressing dendrites may be raised by engineering solid-electrolyte interfaces with higher Li diffusivities. Our work sheds more light on battery degradation and is important for fast charging under extreme conditions.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L9b0052

This research / project is supported by the National Research Foundation Singapore - 2023 Singapore–China Joint Flagship Project (Clean Energy)
Grant Reference no. : NA
Description:
ISSN:
0378-7753
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