Cross-scale parameter transfer and intelligent coupling. This figure showcases how AI-driven simulations and data mining at atomic, mesoscopic and macroscopic scales provide high-fidelity parameters and fundamental insights for the BDA platform, enabling quantitative design and optimization of battery materials and interfaces. (A) Li homogeneous deposition on the Cu surfaces with different indices and the results of surface similarity analysis (SSA). Reproduced with permission from Ref. [54]. Copyright©2022, Wiley-VCH GmbH. (B) Effect of different lithium dendrite morphologies and local current density variance on the self-repair mechanism. Reproduced with permission from Ref. [55]. Copyright©2022, The Author(s). (C) Schematic diagram of the growth mechanism of lithium dendrite relieved by external pressure. Reproduced with permission from Ref. [56]. (D) Dendrite morphology in electrochemical system with different lithium-ion transport parameters (diffusion coefficient of Li⁺ in electrode (D_s′), diffusion coefficient of Li⁺ in electrolyte (D56_l′), electronic conductivity of electrode (σ_s′), ionic conductivity of electrolyte (σ_l′)) at 7 s. Reproduced with permission from Ref. [64]. Copyright©2024, Editorial office of Energy Storage Science and Technology. (E) Level sets of the order parameter ζ (phase parameter) in a small box around the electrode/electrolyte transition zone (ζ₀<ζ₁). Reproduced with permission from Ref. [65]. Copyright©2025, The Author(s). (F) Database-supported high-throughput screening framework for interlayer materials at the Li|SSE interface. (The framework consists of four sequential screening steps to identify promising interlayer material candidates. The gray, blue, yellow, and green sections represent the screening for thermodynamic stability, electrochemical stability, self-limiting reaction behavior, and ionic conductivity, respectively). Reproduced with permission from Ref. [61]. Copyright©2025, The Author(s). (G) Artificial neural network with 10 input variables and two hidden layers. Reproduced with permission from Ref. [62]. Copyright©2018, The Royal Society of Chemistry. (H) A data science approach for advanced solid polymer electrolyte design. Reproduced with permission from Ref. [66]. Copyright©2020 Elsevier B.V. (I) Equilibrium EO-Li⁺ separation (r_EO+^eq) as a function of S_EO+. The inset shows the configuration used to estimate the value of r_EO+^eq (hydrogens not shown for clarity) and the resulting values of ρ after BO. Reproduced with permission from Ref. [63]. Copyright©2023, American Chemical Society.