Film dielectric capacitors have been widely used in high-power electronic equipment. The design of microstructure and the choice of fillers play an important role in nanocomposites' energy storage density. Machine learning methods can classify and summarise the limited data and then explore the promising composite structure. In this work, a dataset has been established, which contained a large amount of data on the maximum energy storage density of nanocomposites. Though using processed visual image information to express the internal information of composite, the prediction accuracy of the prediction models built by three machine learning algorithms increase from 84.1% to 91.9%, 80.9% to 68.9%, 70.6% to 81.6%, respectively. By calculating the branch weight in the random forest prediction model, the influence degree of different descriptors on the energy storage performance of nanocomposites is analysed. A total of 10 groups of composites with different structure and filler amount were prepared in the laboratory, which were used to verify the reliability of prediction models. Finally, the effective filler's structure is explored by three prediction models and some suggestions for the interface design of filler are given.

中文翻译：

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用于储能应用的聚合物纳米复合材料的机器学习和微观结构设计

薄膜介质电容器已广泛应用于大功率电子设备中。微观结构的设计和填料的选择对纳米复合材料的储能密度起着重要作用。机器学习方法可以对有限的数据进行分类和总结，然后探索有前途的复合结构。在这项工作中，已经建立了一个数据集，其中包含大量关于纳米复合材料的最大能量存储密度的数据。三种机器学习算法构建的预测模型虽然利用处理后的视觉图像信息来表达合成的内部信息，但其预测准确率分别从84.1%提高到91.9%、80.9%提高到68.9%、70.6%提高到81.6%。通过计算随机森林预测模型中的分支权重，分析了不同描述符对纳米复合材料储能性能的影响程度。实验室共制备了10组不同结构和填充量的复合材料，用于验证预测模型的可靠性。最后，通过三种预测模型探索了有效填料的结构，并对填料的界面设计提出了一些建议。