Predicting dendrite growth in lithium metal batteries through iterative neural networks and voltage embedding

Abstract With lithium-ion energy density nearing its limits, next-generation storage requires an atomic-scale understanding of dendrites and the solid-electrolyte interphase evolution. Conventional simulations remain computationally prohibitive, whereas machine learning typically predicts macroscopic metrics rather than ion dynamics. We present a deep learning framework that couples a one-dimensional convolutional network with iterative training and a physics-based voltage embedding to forecast ion positions, charge distributions, and dendritic morphology over repeated charge and discharge cycles in lithium metal batteries. The model achieves a mean error of 1.53% for atomic positions and reduces computation time from 18 h (molecular dynamics simulation) to 25 min (proposed framework). It preserves redox trends across cycles and reproduces electrolyte-dependent dendrite suppression (Dice similarity coefficient 0.90; mean absolute percentage error < 2%). The approach offers a practical surrogate for time-series atomistic simulation and supports internal-state screening, failure diagnosis, and the design of next-generation systems.