Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel
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 app...
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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/21/2804 |
| _version_ | 1797512070818693120 |
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| author | Jing-Ang Zhu Yetong Jia Jincheng Lei Zishun Liu |
| author_facet | Jing-Ang Zhu Yetong Jia Jincheng Lei Zishun Liu |
| author_sort | Jing-Ang Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T05:56:42Z |
| format | Article |
| id | doaj.art-45f51acdab934d4abd0d001bd3104004 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T05:56:42Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-45f51acdab934d4abd0d001bd31040042023-11-22T21:19:06ZengMDPI AGMathematics2227-73902021-11-01921280410.3390/math9212804Deep Learning Approach to Mechanical Property Prediction of Single-Network HydrogelJing-Ang Zhu0Yetong Jia1Jincheng Lei2Zishun Liu3International Center for Applied Mechanics, State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInternational Center for Applied Mechanics, State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInternational Center for Applied Mechanics, State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaInternational Center for Applied Mechanics, State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaHydrogel 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.https://www.mdpi.com/2227-7390/9/21/2804deep learninghydrogel networkmechanical propertyconvolutional neural networkself-avoiding walk |
| spellingShingle | Jing-Ang Zhu Yetong Jia Jincheng Lei Zishun Liu Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel Mathematics deep learning hydrogel network mechanical property convolutional neural network self-avoiding walk |
| title | Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel |
| title_full | Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel |
| title_fullStr | Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel |
| title_full_unstemmed | Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel |
| title_short | Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel |
| title_sort | deep learning approach to mechanical property prediction of single network hydrogel |
| topic | deep learning hydrogel network mechanical property convolutional neural network self-avoiding walk |
| url | https://www.mdpi.com/2227-7390/9/21/2804 |
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