As mentioned earlier, one of the significant challenges in battery design is the slow ionic transport in porous electrodes. While progress has been made in designing low-tortuosity batteries through vertical channels and gradient active materials,(88-91) optimal solutions have yet to be found. Although topological optimization has been applied to search for suitable electrode structures, time-dependent charging problems and the time-consuming nature of solving 3D finite element models remain obstacles. To address these issues, Sui et al. drew inspiration from nature and introduced efficient vascular channels into the porous electrode to enhance ionic transport and mitigate non-uniform reaction rate distribution. (Figure ) The vertical central channels and gradient branch channels reduced tortuosity and increased reaction penetration depth. Given the large number of possible vascular channel designs, the researchers developed a DL pipeline to accelerate computation speed. The neural network, informed by the geometric factors of the electrode, speeded up computation for all possible electrode designs and corresponding charging curves by 84 times, compared to the conventional finite element method. Furthermore, the researchers developed an inverse design workflow to find the optimal electrode structure from the total library of computed designs under different application scenarios, such as varying charging rates. The workflow delivered a customized package containing electrode geometric factors, charging curves, charging capacity, energy density, etc. With the aid of bio-inspired vascularized design and DL-based prediction, the optimized porous electrode demonstrated a 66% improvement in capacity under 3.2C constant current charging. This work could potentially inspire advancements in the experimental and theoretical aspects of fastcharging batteries in the future. ) This is achieved by integrating generative adversarial neural networks and deep neural networks with physical knowledge to accurately predict current density. The framework was demonstrated in two design philosophies, inverse and forward design, for solid oxide fuel cell (SOFC) anodes. The results showed that π learning can generate electrode microstructures with the globally optimal electrode microstructure. The physical and electrochemical insights obtained by prediction can guide the rational design of SOFC electrodes. The framework can be easily transferred to the design of other porous electrodes in various electrochemical devices, such as fuel cells and batteries. The proposed π learning can be further enhanced by involving high-dimensional multi-physics computation to inform the generative model.