Revealing Morphology Evolution of Lithium Dendrites by Large-Scale Simulation Based on Machine Learning Force Field

Solving the dendrite growth problem is critical for the development of lithium metal anode for high-capacity batteries. In this work, a machine learning force field model in combination with a self-consistent continuum solvation model is used to simulate the morphology evolution of dendrites in a working electrolyte environment. The dynamic evolution of the dendrite morphology can be described in two stages. In the first stage, the energy reduction of the surface atoms induces localized reorientation of the originally single-crystal dendrite and the formation of multiple domains. In the second stage, the energy reduction of internal atoms drives the migration of grain boundaries and the slipping of crystal domains. The results indicate that the formation of multiple domains might help to stabilize the dendrite, as a higher temperature trajectory in a single crystal dendrite without domains shows a higher dendrite collapsing rate. Several possible modes of morphological evolutions are also investigated, including surface diffusion of adatoms and configuration twists from [100] exposed surfaces to [110] exposed surfaces. In summary, reducing the surface and grain boundary energy drives the morphology evolution. Based on the analysis of these driving forces, some guidelines are suggested for designing a more stable lithium metal anode.

Automated summary

This study simulates the morphology evolution of dendrites in an electrolyte environment using a machine learning force field model and a self-consistent continuum solvation model. It suggests that reducing the surface and grain boundary energy can stabilize the dendrite morphology. Furthermore, the formation of multiple domains can also increase the stability of dendrites.