Modeling of Lithium Dendrite Growth in Ionic Liquids with Lattice Monte Carlo Simulation Method and Deep Neural Networks

We develop a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the molecular properties of ionic liquids (ILs) on lithium dendrite growth. Our simulations show that the size asymmetry between the cation and anion, the dielectric constant, and the volume fraction of ILs are critical factors to significantly suppress the dendrite growth, primarily due to substantial changes in electric-field screening. Specifically, the volume fraction of ILs has the optimal value for the dendrite suppression. The present simulation method indicates potential challenges for the model extension to macroscopic systems. Therefore, we also develop ensemble neural networks (ENNs) in machine learning methods with training datasets derived from the MC simulations by considering the input descriptors with the dielectric constant, the model parameter for the fractal dimension of the dendrite, the volume fraction of ILs, and the applied voltage. 200 samples are required for each data point for good statistical convergence in averaging the simulation data. In contrast, our ENNs can predict the highly nonmonotonic trend of the simulation results from only 20 samples for each data point, thus significantly reducing the required computation time.