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Generative Artificial Intelligence for designing multi-scale hydrogen fuel cell catalyst layer nanostructures

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posted on 2025-05-09, 11:31 authored by Zhiqiang NiuZhiqiang Niu, Wanhui Zhao, Hao Deng, Lu TianLu Tian, Valerie PinfieldValerie Pinfield, Pingwen Ming, Yun Wang
Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and cost-effectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vector-variational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, e.g., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices.

Funding

National Natural Science Foundation of China (grant no. 52206187)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Published in

ACS Nano

Volume

18

Issue

31

Pages

20504 - 20517

Publisher

American Chemical Society (ACS)

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This publication is licensed under CC-BY 4.0

Acceptance date

2024-07-03

Publication date

2024-07-10

Copyright date

2024

ISSN

1936-0851

eISSN

1936-086X

Language

  • en

Depositor

Prof Valerie Pinfield. Deposit date: 9 January 2025

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