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Deep kernel Bayesian optimisation for closed-loop electrode microstructure design with user-defined properties
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DOI：10.1016/j.egyai.2025.100608.png)
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Abstract
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• Closed-loop microstructure design integrating a trained GAN and Bayesian optimization. • GP as a surrogate model to map the generator’s latent space to electrode properties. • Simultaneous maximization of correlated morphological and transport properties. • Constrained optimization enhances microstructural properties while maintains loading. • Microstructural design of tailored electrodes through latent space optimization.
Keywords:
Gaussian processes
Deep kernel Bayesian optimisation
Lithium-ion battery cathode
Multi-phase porous electrodes
Microstructure design
Specific surface area
Relative diffusivity
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