Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs

2025/06/07 by Andrea Gayon-Lombardo, Ehecatl Antonio del Rio‐Chanona, Gayon-Lombardo, Andrea +5
Computer Science · Materials Science · #Advanced Multi-Objective Optimization Algorithms #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2508.00833

openalex publication_date 2025/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Real-Time Optimization Meets Bayesian Optimization and Derivative-Free\n Optimization: A Tale of Modifier Adaptation
• Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
• Stochastic reconstruction of an oolitic limestone by generative adversarial networks
• Optimizing the Latent Space of Generative Networks
• NIPS 2016 Tutorial: Generative Adversarial Networks
• A guide to convolution arithmetic for deep learning
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Gaussian Processes: A Quick Introduction
• Taking the Human Out of the Loop: A Review of Bayesian Optimization

Related

• Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
• Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
• Image-Guided Microstructure Optimization using Diffusion Models: Validated with Li-Mn-rich Cathode Precursors
• Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations
• Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing