π learning: a performance‐Informed framework for microstructural electrode design
Author(s)
Type
Journal Article
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.
Date Issued
2023-05-05
Online Publication Date
2023-04-28T13:49:01Z
Date Acceptance
2023-02-14
ISSN
1614-6832
Publisher
Wiley
Start Page
1
End Page
14
Journal / Book Title
Advanced Energy Materials
Volume
13
Issue
7
Copyright Statement
© 2023 The Authors. Advanced Energy Materials published by Wiley-VCH
GmbH. This is an open access article under the terms of the Creative
Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
GmbH. This is an open access article under the terms of the Creative
Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
License URI
Identifier
https://onlinelibrary.wiley.com/doi/full/10.1002/aenm.202300244
Publication Status
Published
Article Number
2300244
Date Publish Online
2023-03-09