用深度学习+计算模拟揭示电池枝晶生长机制，提升预测精度。
Insights into dendritic growth mechanisms in batteries: A combined machine learning and computational study
- 构建双模型：标准CNN与融合物理参数的CNN-2
- CNN-2预测准确率显著提升，捕捉枝晶动态演化
- 适合电池安全研究与材料设计人员参考
近年来，研究人员日益关注电池作为高效、低成本能源存储与供应方案，因其高能量密度、低成本和环境耐受性。然而，枝晶生长已成为电池发展的重要障碍。充放电过程中过度的枝晶生长会导致电池短路、电化学性能下降、循环寿命缩短及异常放热事件。因此，理解枝晶生长过程成为研究关键挑战。本研究结合机器学习与计算方法，采用二维卷积神经网络（CNN）模型，构建两个不同计算机模型以预测电池中的枝晶生长。CNN-1模型使用标准卷积神经网络技术进行枝晶生长预测；CNN-2模型则引入额外的物理参数以增强模型鲁棒性。结果表明，CNN-2显著提高预测准确性，深入揭示物理因素对枝晶生长的影响。该改进模型有效捕捉枝晶形成的动态特性，具备高精度与高灵敏度。研究成果有助于推动更安全、可靠的储能系统发展。
原文摘要 · Abstract (English)
In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. However, the issue of dendrite growth has emerged as a significant obstacle in battery development. Excessive dendrite growth during charging and discharging processes can lead to battery short-circuiting, degradation of electrochemical performance, reduced cycle life, and abnormal exothermic events. Consequently, understanding the dendrite growth process has become a key challenge for researchers. In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods. We developed two distinct computer models to predict dendrite growth in batteries. The CNN-1 model employs standard convolutional neural network techniques for dendritic growth prediction, while CNN-2 integrates additional physical parameters to enhance model robustness. Our results demonstrate that CNN-2 significantly enhances prediction accuracy, offering deeper insights into the impact of physical factors on dendritic growth. This improved model effectively captures the dynamic nature of dendrite formation, exhibiting high accuracy and sensitivity. These findings contribute to the advancement of safer and more reliable energy storage systems.
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