The use of machine learning to predict rheological properties of polymers has great potential to facilitate the characterization of novel materials. Here, we have suggested the analogy between the double reptation (DR) and the deep neural network model. The double reptation model itself can be the special case of the deep learning method; linear activation function, and identical sets of weights for the two hidden layers are the characteristics of the double reptation model. The identical sets of weights in the double reptation model are related with the molecular weight distribution (MWD). We first generated ground truth data based on double reptation model. Then, we analyzed the dataset with reptation-guided deep neural network (RGDNN). We showed that the RGDNN model is available to determine entanglement molecular weight (plateau modulus), and monomeric friction factors from the simulated experimental rheological data (prepared using DR model) without any additional information. Overall, a noteworthy conceptual improvement in the determination of major factors that determine the rheological behavior of ultrahigh molecular weight polyethylene (UHMWPE) gels has been achieved.

使用机器学习来预测聚合物的流变特性在促进新型材料的表征方面具有巨大的潜力。在这里，我们提出了双爬行（DR）和深度神经网络模型之间的类比。双爬行模型本身可以是深度学习方法的特例；线性激活函数和两个隐藏层的相同权重集是双爬行模型的特征。双蠕动模型中相同的权重组与分子量分布（MWD）相关。我们首先基于双爬行模型生成地面实况数据。然后，我们使用爬行引导深度神经网络（RGDNN）分析数据集。我们表明，RGDNN 模型可用于根据模拟实验流变数据（使用 DR 模型准备）确定缠结分子量（平台模量）和单体摩擦系数，而无需任何其他信息。总体而言，在确定决定超高分子量聚乙烯（UHMWPE）凝胶流变行为的主要因素方面，已经取得了值得注意的概念改进。