近年来,机器学习在超级电容器(SCs)和电容去离子(CDI)领域的研究取得了显著进展。通过系统分析机器学习(ML)技术在电极材料设计、电化学性能优化以及去离子过程调控等方面的应用,揭示了其在加速新材料开发和性能提升方面的关键作用。重点探讨了监督学习、无监督学习、集成学习和深度学习等算法在SCs和CDI研究中的具体应用案例,并对当前面临的数据质量、模型可解释性等挑战进行了剖析。最后,对机器学习在SCs和CDI领域未来的发展方向进行了展望,旨在为电化学储能与脱盐技术的智能化研究提供重要参考。

In recent years,significant research progress of machine learning (ML) has been achieved in the fields of supercapacitors (SCs) and capacitive deionization (CDI).This analysis systematically examined the application of machine learning (ML) techniques in electrode material design,optimization of electrochemical performance,and regulation of the deionization process,demonstrating its crucial role in accelerating new material discovery and performance enhancement.The discussion focused on specific use cases of algorithms,including supervised learning,unsupervised learning,ensemble learning,and deep learning,in SCs and CDI research.Current challenges such as data quality and model interpretability were also analyzed.Finally,future research directions for ML in these fields were outlined,aiming to provide a valuable reference for the intelligent development of electrochemical energy storage and desalination technologies.