Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.
Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.
Chem Rev. 2022 Jun 22;122(12):10899-10969. doi: 10.1021/acs.chemrev.1c00108. Epub 2021 Sep 16.
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
这是一篇关于人工智能/机器学习(AI/ML)方法在电池研究中应用的批判性综述。它旨在提供一个全面、权威、批判性的综述,但又易于理解,以引起电池界的广泛兴趣。它讨论了将 AI/ML 作为加速下一代电池设计和优化的工具的概念、方法、工具、成果和挑战——这是当前的热门话题。它旨在让化学和电化学能源科学领域的研究人员更容易接触到这些工具,并涵盖不同电池研发方面的完整性。