The increasing demand for polymer dielectrics in different scenarios, such as electrical insulation cable, requires elevated electrical and mechanical properties. In order to understand the relationship between mechanical properties and the structure of grafted monomers in a grafted polypropylene (PP) system, a design framework based on the transfer-learning method has been proposed for designing grafted side chain structure in PP system. 22 samples of polypropylene-grafted polymer are synthesized. Through a well-trained multi-layer perceptron model for predicting glass transition temperature (Tg), mechanical properties of the grafted PP have been well predicted by the transfer-learning method. Meanwhile, the grafting group structures are divided into nine substructures. By assembling these substructures, the chemical space is established for prediction. Moreover, candidates with satisfied deep traps and flexible mechanical properties are screened out for reference. This work turns qualitative understanding into a quantitative analysis based on mathematical models and broadens the application of polymer materials design with a small dataset.

中文翻译：

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通过使用小型数据库的迁移学习预测接枝聚丙烯体系的机械性能

电绝缘电缆等不同场景对聚合物电介质的需求不断增加，需要更高的电气和机械性能。为了理解机械性能与接枝聚丙烯 (PP) 体系中接枝单体结构之间的关系，提出了一种基于迁移学习方法的设计框架，用于设计 PP 体系中的接枝侧链结构。合成了 22 个聚丙烯接枝聚合物样品。通过用于预测玻璃化转变温度 (Tg) 的训练有素的多层感知器模型，转移学习方法可以很好地预测接枝 PP 的机械性能。同时，接枝基团结构分为九个子结构。通过组装这些子结构，化学空间是为预测而建立的。此外，筛选出具有令人满意的深陷阱和柔性机械性能的候选者以供参考。这项工作将定性理解转化为基于数学模型的定量分析，并通过小数据集拓宽了聚合物材料设计的应用。