Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties
File(s)
Published version
Author(s)
Gayon-Lombardo, Andrea
Del Rio Chanona, Antonio
Pino-Munoz, Catalina
Brandon, Nigel P
Type
Journal Article
Abstract
The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties.
Date Issued
2025-12-01
Date Acceptance
2025-09-02
Citation
Energy and AI, 2025, 22
ISSN
2666-5468
Publisher
Elsevier
Journal / Book Title
Energy and AI
Volume
22
Copyright Statement
© 2025 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Publication Status
Published
Article Number
100608
Date Publish Online
2025-09-10