Optimization of 3D Battery Geometries Using a Combination of Accurate Energy Prediction Models and Automatic Geometry Generator

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In order to enlarge the IoT ecosystem, there is a growing need for microbatteries with high areal energy density. A promising approach to improve areal energy density includes using three-dimensional (3D) electrodes, which allow for the holding of more active materials without increasing the internal resistance. Nonetheless, pinpointing the optimal 3D geometry is a challenging task, reliant on the printable materials, the resolution of the production machinery, and the usage of the battery. This constitutes a multiobjective optimization problem, introducing substantial difficulties even when computer simulations are employed because of their high computational cost.To overcome this challenge, we propose a novel approach for the automated identification of optimal 3D battery geometries, which combines quick and accurate energy prediction models with an automatic geometry generator (AGG). [1-4] The energy prediction models employ simple principal component regression, which results in a significantly reduced computational cost. [2] AGG constructs a 3D geometry by filling the empty cell space with cuboid-shaped electrodes (electrode elements). The placement of each electrode element is determined automatically through the Monte Carlo tree search (MCTS) algorithm, leading to the designation of this method as AGG-MCTS. Given that the dimensions of the electrode elements are determined following the resolution of using fabrication equipment, such as 3D printers, our method ensures the generation of only manufacturable geometries. The combination of the energy prediction models and AGG-MCTS enables the rapid identification of optimal 3D battery geometries, without using human intuition and experience.In this study, we demonstrate the efficacy of our approach through its application to two standard electrode pairs: LiFePO4/Li4Ti5O12 (LFP/LTO) and LiNi0.5Mn0.3Co0.2O2/graphite (NMC/Graphite). Despite the distinct discharging behaviors of these systems, our regression models accurately predict the discharge-current-dependent energies of both systems.[4] By integrating these models with the AGG-MCTS, we successfully identify frontier solutions and obtain optimal geometries that display 30% - 40% greater energy than the current state of the art. Notably, optimal geometries vary in response to the discharge current and the specific electrode combination. Our approach uniquely facilitates the discovery of material- and discharge-current-dependent optimal geometries for the first time. This advancement underscores the significance of custom-designed batteries for a variety of IoT applications and highlights our method’s potential to enable such designs.[1] Miyamoto, et al., iScience, 23, 101317 (2020).[2] Miyamoto, et al., Cell. Rep. Phys. Sci., 2, 100504 (2021).[3] Miyamoto, et al., J. Power. Sources, 536, 231473 (2022).[4] Miyamoto, in preparation (2024).