π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Abstract

Designing high‐performance porous electrodes is the key to next‐generation electrochemical energy devices. Current machine‐learning‐based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance‐orientated electrode design is challenging because the current data driven approaches do not accurately extract high‐dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance‐informed deep learning framework, termed π learning, which enables performance‐informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physics‐informed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi‐physics, multi‐scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance‐driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.