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<p>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.</p>

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Framework for digital synthesis of battery electrode microstructure using inverse generative machine learning algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

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  • Sragwin Nitturi,
  • Ajay Rama Raju Datla,
  • Kyu Taek Cho,
  • Venkat Srinivasan

摘要

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.