分割
电极
电池(电)
深度学习
计算机科学
材料科学
人工智能
石墨
锂离子电池
锂(药物)
微观结构
纳米技术
复合材料
化学
物理
内分泌学
物理化学
功率(物理)
医学
量子力学
作者
Simon Müller,Christina Sauter,Ramesh Shunmugasundaram,Nils Wenzler,Vincent De Andrade,Francesco De Carlo,Ender Konukoğlu,Vanessa Wood
标识
DOI:10.1038/s41467-021-26480-9
摘要
Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.
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