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<p>In the development of battery science, machine learning (ML) has been widely employed to predict material properties, monitor morphological variations, learn the underlying physical rules and simplify the material-discovery processes. However, the widespread adoption of ML in battery research has encountered limitations, such as the incomplete and unfocused databases, the low model accuracy and the difficulty in realizing experimental validation. It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the application-oriented perspective. We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies.</p>

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Application-oriented design of machine learning paradigms for battery science

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月2日

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  • Ying Wang

摘要

In the development of battery science, machine learning (ML) has been widely employed to predict material properties, monitor morphological variations, learn the underlying physical rules and simplify the material-discovery processes. However, the widespread adoption of ML in battery research has encountered limitations, such as the incomplete and unfocused databases, the low model accuracy and the difficulty in realizing experimental validation. It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the application-oriented perspective. We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies.