Applying data-driven machine learning to studying electrochemical energy storage materials

doi:10.19799/j.cnki.2095-4239.2022.0051

Abstract

Abstract:

Materials are key to energy storage batteries. With experimental observations, theoretical research, and computational simulations, data-driven machine learning should provide a new paradigm for electrochemical energy storage material research and development. Its advantages include rapid capture of the complex structure-activity relationship between material composition, structure, process, and performance. In this study, the latest developments in employing machine learning in electrochemical energy storage materials are reviewed systematically from structured and unstructured data-driven perspectives. The material databases from China and abroad are summarized for electrochemical energy storage material use, and data collection and quality inspection problems are analyzed. Data-driven machine learning workflows and applications in electrochemical energy storage materials are demonstrated. They contain data collection, feature engineering, and machine learning modeling under structured data, and the model construction and application under unstructured data of graphics, representation images, and literature. Three principal contradictions and countermeasures faced by machine learning in electrochemical energy storage materials are highlighted: the contradiction and coordination of high-dimensional and small sample data, the contradiction and unity of model complexity and ease of use, and the contradiction and contradiction fusion between model learning results and expert experience. We highlighted that "Machine Learning Embedded with Domain Knowledge" construction should reconcile these contradictions. This review provides a reference for applying machine learning in electrochemical energy storage materials' design and performance optimization.

Key words: electrochemical energy storage materials, machine learning, materials database, domain knowledge

CLC Number:

TP 181

Siqi SHI, Zhangwei TU, Xinxin ZOU, Shiyu SUN, Zhengwei YANG, Yue LIU. Applying data-driven machine learning to studying electrochemical energy storage materials[J]. Energy Storage Science and Technology, 2022, 11(3): 739-759.

Figures/Tables 2

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