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首页> 外文期刊>Advanced energy materials >Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning
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Guiding the Design of Heterogeneous Electrode Microstructures for Li-Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

机译：引导锂离子电池异构电极微结构的设计：微观成像，预测建模和机器学习

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摘要

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.
机译：锂离子电池材料的电化学和力学性能严重依赖于其3D微观结构特性。对随机微观结构作用的作用的定量理解对于预测材料性质和引导合成过程至关重要。此外，定制微观结构形态也是实现锂离子电池最佳电化学和机械性能的可行方法。为了便于建立微观结构解决的建模和设计方法，介绍了一种审查涵盖了微观结构和电化学现象的空间和时间分辨成像，微观结构统计表征和随机重建，用于性能预测的微观结构解决，以及微观结构设计的机器学习这里。提出了对应用实验数据，建模和机器学习的未解决挑战和机遇的观点，提高了改善材料的理解，识别锂离子细胞增强性能的途径。

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