Framework for digital synthesis of battery electrode microstructure using inverse generative machine learning algorithm
Year: 2026
Type: article
Abstract: The microstructure of electrodes in lithium-ion batteries strongly influences the transport and electrochemical behavior of reactive species, ultimately affecting overall battery performance. While numerous studies have investigated how variations in electrode microstructure influence cell behavior, research aimed at directly generating electrode microstructure images from target performance inputs remains limited. Enabling such digital synthesis would represent a significant shift in current research practices, offering a pathway to accelerate battery innovation by providing explicit, data-driven guidelines for designing electrodes tailored to desired performance characteristics. As an initial step toward this objective, this study presents a data-driven framework that integrates physics-based battery modeling with advanced deep learning algorithms to enable controllable generation of electrode microstructures. Using this approach, realistic microstructures of graphite and composite anode electrodes can be successfully generated for the target performance indices. The proposed framework combines battery performance data generated from a physics-based model, time-series machine learning models for performance prediction, and an image generative model for microstructure synthesis. This unified approach can serve as a practical tool for battery developers, accelerating microstructure-informed electrode design. Furthermore, the framework can be extended to other cell components, providing a novel strategy for digitally generating high-performance designs across the entire battery system.
Source: npj Computational Materials
Institutions: Northern Illinois University, Argonne National Laboratory
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
OpenAlex ID: W7165193039
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