Mesoscale Machine Learning Analytics for Electrode Property Estimation
Mesoscale Machine Learning Analytics for Electrode Property Estimation
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DOI：
10.1021/acs.jpcc.2c04432
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发表时间：
2022-09-01
影响因子：
3.7
通讯作者：
Mukherjee, Partha P.
中科院分区：
文献类型：
--
作者：
Kabra, Venkatesh;Birn, Brennan;Mukherjee, Partha P.
具有高面积和体积能量密度的下一代电池的开发需要使用高活性材料质量负载电极。这通常会降低功率密度，但对快速充电的推动推动了微结构设计的创新，以提高运输和电化学转化效率。这需要精确有效的电极性质估计，例如弯曲度、电子电导率和界面面积。仅从实验和3D介观尺度模拟获得这些信息是耗时的，而经验关系仅限于简化的微观结构几何形状。在这项工作中，我们提出了一种替代路线的快速表征电极微观结构的有效性能，使用机器学习（ML）。使用锂离子电池石墨阳极电极作为一个范例系统，我们生成了一个全面的数据集的SIM;17 000电极微观结构。这些由各种形状，尺寸，方向和化学成分组成，并使用3D中尺度模拟来表征其有效特性。通过计算一组全面的物理描述符并消除冗余特征，实现了每个微结构的低维表示。基于多孔电极微观结构特征的介观ML分析，对于有效性质估计，预测精度达到90%以上。
The development of next-generation batteries with high areal and volumetric energy density requires the use of high active material mass loading electrodes. This typically reduces the power density, but the push for rapid charging has propelled innovation in microstructure design for improved transport and electrochemical conversion efficiency. This requires accurate effective electrode property estimation, such as tortuosity, electronic conductivity, and interfacial area. Obtaining this information solely from experiments and 3D mesoscale simulations is time-consuming while empirical relations are limited to simplified microstructure geometry. In this work, we propose an alternate route for rapid characterization of electrode micro -structural effective properties using machine learning (ML). Using the Li-ion battery graphite anode electrode as an exemplar system, we generate a comprehensive data set of & SIM;17 000 electrode microstructures. These consist of various shapes, sizes, orientations, and chemical compositions, and characterize their effective properties using 3D mesoscale simulations. A low dimensional representation of each microstructure is achieved by calculating a set of comprehensive physical descriptors and eliminating redundant features. The mesoscale ML analytics based on porous electrode microstructural characteristics achieves prediction accuracy of more than 90% for effective property estimation.