Hybrid modeling and efficient simulation. These studies demonstrate the power of hybrid AI-physics modeling and data-driven surrogate models within the BDA framework to achieve a balance between simulation accuracy and computational efficiency, which is crucial for rapid design iteration and reliable performance prediction. (A) A schematic diagram of the PINN architecture for the P2D model of a Li-ion battery cell. Reproduced with permission from Ref. [86]. Copyright©2025, Elsevier B.V. (B) A lightweight two-stage physics-informed neural network method for state of health (SOH) estimation. Reproduced with permission from Ref. [87]. (C) Schematic illustration of interface failure in solid-state battery and framework of machine learning model. (a) Schematic of a solid-state lithium metal battery and interface failure mechanisms. (b) Left panel: defects in solid-state electrolyte and corresponding stress fields calculated by finite element analysis. Statistical analysis identified curvature C_r and eccentricity ratio E_c as two key defect descriptors. Right panel: prior knowledge was quantitatively analyzed (including aspect ratio AR, symmetry S, and size-density δ) to generate the prior stress field map. (c) Illustration for three machine learning models, convolutional neural network (CNN), residual neural network (ResNet) and UNet. (d) Framework of SE-UNet model developed in this work. (e) Performance comparison between SE-UNet model and other models. Reproduced with permission from Ref. [88]. (D) Overview of the model development. Reproduced with permission from Ref. [89]. Copyright©2017, Elsevier B.V. (E) Mesostructures for three different compositions (85:15, 90:10, and 95:5) shown before solvent evaporation as equilibrated slurry mesostructures (i–iii) and after solvent evaporation and equilibration-formed electrodes (iv–vi). Light green particles represent the CBD particles with solvent, and bright green is used to denote the CBD particles after solvent evaporation; different colors (blue, yellow, light yellow, white, and purple) are used to show the five AM sizes as per the particle size distribution. Reproduced with permission from Ref. [90]. Copyright©2017, American Chemical Society.