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机器学习中锂电池材料数据问题的解决方案：概述与未来展望

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook.

作者信息

Xue Pengcheng, Qiu Rui, Peng Chuchuan, Peng Zehang, Ding Kui, Long Rui, Ma Liang, Zheng Qifeng

机构信息

School of Chemistry, Guangzhou Key Laboratory of Materials for Energy Conversion and Storage, South China Normal University, Guangzhou, 510006, China.

School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.

出版信息

Adv Sci (Weinh). 2024 Dec;11(48):e2410065. doi: 10.1002/advs.202410065. Epub 2024 Nov 18.

DOI:10.1002/advs.202410065
PMID:39556707
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11672295/
Abstract

The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges.

摘要

机器学习（ML）技术在锂电池领域的应用相对较新，在发现新材料、优化电化学过程以及预测电池寿命方面具有巨大潜力。然而，ML预测的准确性强烈依赖于基础数据，而锂电池材料的数据面临许多挑战，如多源、异质性、高维度和小样本量。通过对现有文献的系统综述，提出了以下几种有效的数据处理策略：分类与提取、筛选与探索、降维和生成、建模与评估以及纳入领域知识，旨在提高数据质量、模型可靠性和可解释性。此外，还强调了其他解决数据质量的可能策略，如数据库管理技术和数据分析方法。最后，对ML数据处理方法的发展进行了展望。这些方法不仅适用于锂电池材料的数据，也为电催化、电化学腐蚀、高熵合金等面临类似数据挑战的领域赋予了重要的参考意义。

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