自愈水凝胶
介观物理学
深度学习
材料科学
人工智能
计算机科学
人工神经网络
卷积神经网络
网络模型
生物系统
物理
量子力学
生物
高分子化学
作者
Jing-Ang Zhu,Yetong Jia,Jincheng Lei,Zishun Liu
出处
期刊:Mathematics
[MDPI AG]
日期:2021-11-04
卷期号:9 (21): 2804-2804
被引量:16
摘要
Hydrogel has a complex network structure with inhomogeneous and random distribution of polymer chains. Much effort has been paid to fully understand the relationship between mesoscopic network structure and macroscopic mechanical properties of hydrogels. In this paper, we develop a deep learning approach to predict the mechanical properties of hydrogels from polymer network structures. First, network structural models of hydrogels are constructed from mesoscopic scale using self-avoiding walk method. The constructed model is similar to the real hydrogel network. Then, two deep learning models are proposed to capture the nonlinear mapping from mesoscopic hydrogel network structural model to its macroscale mechanical property. A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels under uniaxial tension. Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates that the deep learning models are able to capture the internal relationship between complex network structures and mechanical properties. We hope this approach can provide guidance to structural design and material property design of different soft materials.
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