Inverse Design of Structured Electrodes in Lithium Metal Batteries: Integrated High‐Throughput Phase‐Field Modeling and Machine Learning

Abstract Lithium metal is a promising anode for next‐generation batteries; however, uncontrolled dendrite growth severely hinders its practical application and long‐term stability. Structural design of electrodes and separators provides a viable strategy to regulate dendrite formation. In this study, a phase‐field model is employed to simulate dendrite evolution under galvanostatic conditions, offering mesoscale insights into the deposition process. A range of structured electrode and separator geometries is designed, and high‐throughput simulations are conducted to capture their dynamic behavior during charging. This generates a comprehensive dataset linking structural features to key battery performance metrics, including capacity and lifespan. Several machine learning regression models are trained and evaluated to extract predictive relationships between structure and performance. To enable inverse design, the dataset is further augmented using deep neural networks and coupled with optimization algorithms—including genetic algorithms—for both single‐ and multi‐objective scenarios. The resulting framework facilitates efficient structural optimization of lithium metal battery architectures. Overall, this work establishes a data‐driven paradigm that integrates phase‐field modeling, high‐throughput simulation, and machine learning to guide the rational design of structured electrodes and separators for dendrite suppression and performance enhancement.