Materials characterization is fundamental to our understanding of lithium ion battery electrodes and their performance limitations. Advances in laboratory-based characterization techniques have yielded powerful insights into the structure–function relationship of electrodes, yet there is still far to go. Further improvements rely, in part, on gaining a deeper understanding of complex physical heterogeneities in the materials. However, practical limitations in characterization techniques inhibit our ability to combine data directly. For example, some characterization techniques are destructive, thus preventing additional analyses on the same region. Fortunately, artificial intelligence (AI) has shown great potential for achieving representative, 3D, multi-modal datasets by leveraging data collected from a range of techniques. In this Perspective, we give an overview of recent advances in lab-based characterization techniques for Li-ion electrodes. We then discuss how AI methods can combine and enhance these techniques, leading to substantial acceleration in our understanding of electrodes.

中文翻译：

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机器学习驱动的电池电极高级表征

材料表征是我们理解锂离子电池电极及其性能限制的基础。基于实验室的表征技术的进步已经对电极的结构-功能关系产生了强有力的见解，但还有很长的路要走。进一步的改进部分取决于对材料中复杂的物理异质性的更深入理解。然而，表征技术的实际局限性限制了我们直接组合数据的能力。例如，一些表征技术具有破坏性，因此无法对同一区域进行额外的分析。幸运的是，人工智能 (AI) 已显示出巨大的潜力，可以通过利用从一系列技术收集的数据来实现具有代表性的 3D 多模态数据集。在这个观点中，我们概述了基于实验室的锂离子电极表征技术的最新进展。然后我们讨论人工智能方法如何结合和增强这些技术，从而大大加快我们对电极的理解。