Imperial College London
Portrait Picture

Dr Billy Wu

Faculty of Engineering, Dyson School of Design Engineering

Reader in Electrochemical Design Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6385billy.wu Website

 
 
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Location

 

1M04Royal College of ScienceSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Niu:2023:10.1002/aenm.202300244,
author = {Niu, Z and Zhao, W and Wu, B and Wang, H and Lin, W and Pinfield, VJ and Xuan, J},
doi = {10.1002/aenm.202300244},
journal = {Advanced Energy Materials},
pages = {1--14},
title = {π learning: a performanceInformed framework for microstructural electrode design},
url = {http://dx.doi.org/10.1002/aenm.202300244},
volume = {13},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Niu,Z
AU  - Zhao,W
AU  - Wu,B
AU  - Wang,H
AU  - Lin,W
AU  - Pinfield,VJ
AU  - Xuan,J
DO  - 10.1002/aenm.202300244
EP  - 14
PY  - 2023///
SN  - 1614-6832
SP  - 1
TI  - π learning: a performanceInformed framework for microstructural electrode design
T2  - Advanced Energy Materials
UR  - http://dx.doi.org/10.1002/aenm.202300244
UR  - https://onlinelibrary.wiley.com/doi/full/10.1002/aenm.202300244
UR  - http://hdl.handle.net/10044/1/103659
VL  - 13
ER  -
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