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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]) |
ISSN | 2198-3844 |
EISSN | 2198-3844 |
发表状态 | 已发表 |
DOI | 10.1002/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 |
URL | 查看原文 |
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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