Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning
2025
发表期刊ADVANCED SCIENCE (IF:14.3[JCR-2023],16.3[5-Year])
ISSN2198-3844
EISSN2198-3844
发表状态已发表
DOI10.1002/advs.202413293
摘要

3D disordered fibrous network structures (3D-DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure-property relationships and stability of biomimetic 3D-DFNS using large datasets generated through procedural modeling, coarse-grained molecular dynamics simulations, and machine learning. Based on these datasets, a network deep reinforcement learning (N-DRL) framework is developed to optimize its stability, effectively balancing weight reduction with the maintenance of structural integrity. The results reveal a pronounced correlation between the total fiber length in 3D-DFNS and its mechanical properties, where longer fibers enhance stress distribution and stability. Additionally, fiber orientation is also considered as a potential factor influencing stress growth values. Furthermore, the N-DRL model demonstrates superior performance compared to traditional approaches in optimizing network stability while minimizing mass and computational cost. Structural integrity is significantly improved through the addition of triple junctions and the reduction of higher-order nodes. In summary, this study leverages machine learning to optimize biomimetic 3D-DFNS, providing novel insights into the design of lightweight, high-strength materials.

关键词biomimetic deep reinforcement learning molecular dynamics simulations network structures stability optimization
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收录类别SCI ; EI
语种英语
资助项目null[52322305] ; null[52473098] ; null[21935002]
WOS研究方向Chemistry ; Science & Technology - Other Topics ; Materials Science
WOS类目Chemistry, Multidisciplinary ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary
WOS记录号WOS:001402365300001
出版者WILEY
EI入藏号20250417748733
EI主题词Deep reinforcement learning
EI分类号101.4 Biomechanics, Bionics and Biomimetics - 1101.2 Machine Learning - 1101.2.1 Deep Learning - 1201.7 Optimization Techniques - 214.1.1 Stress and Strain
原始文献类型Article in Press
文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/483916
专题物质科学与技术学院
物质科学与技术学院_PI研究组_凌盛杰组
物质科学与技术学院_硕士生
物质科学与技术学院_博士生
物质科学与技术学院_PI研究组_刘一凡组
通讯作者Gao, Wenli; Ling, Shengjie
作者单位
1.ShanghaiTech Univ, Sch Phys Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
2.Fudan Univ, Dept Macromol Sci, State Key Lab Mol Engn Polymers, Shanghai 200433, Peoples R China
3.ShanghaiTech Univ, State Key Lab Adv Med Mat & Devices, Shanghai 201210, Peoples R China
4.Shanghai Clin Res & Trial Ctr, Shanghai 201210, Peoples R China
第一作者单位物质科学与技术学院
通讯作者单位物质科学与技术学院;  上海科技大学
第一作者的第一单位物质科学与技术学院
推荐引用方式
GB/T 7714
Yang, Yunhao,Bai, Runnan,Gao, Wenli,et al. Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning[J]. ADVANCED SCIENCE,2025.
APA Yang, Yunhao.,Bai, Runnan.,Gao, Wenli.,Cao, Leitao.,Ren, Jing.,...&Ling, Shengjie.(2025).Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning.ADVANCED SCIENCE.
MLA Yang, Yunhao,et al."Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning".ADVANCED SCIENCE (2025).
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