Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs
SCIE
SCOPUS
- Title
- Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs
- Authors
- BEICHEN LI; Oh, Tae-Hyun; Wojciech Matusik; Deng, Bolei; Shou, Wan; Hu, Yuanming; Luo, Yiyue; Shi, Liang; Matusik, Wojciech
- Date Issued
- 2024-02
- Publisher
- American Association for the Advancement of Science
- Abstract
- The conflict between stiffness and toughness is a fundamental problem in engineering materials design. However, the systematic discovery of microstructured composites with optimal stiffness-toughness trade-offs has never been demonstrated, hindered by the discrepancies between simulation and reality and the lack of data-efficient exploration of the entire Pareto front. We introduce a generalizable pipeline that integrates physical experiments, numerical simulations, and artificial neural networks to address both challenges. Without any prescribed expert knowledge of material design, our approach implements a nested-loop proposal-validation workflow to bridge the simulation-to-reality gap and find microstructured composites that are stiff and tough with high sample efficiency. Further analysis of Pareto-optimal designs allows us to automatically identify existing toughness enhancement mechanisms, which were previously found through trial and error or biomimicry. On a broader scale, our method provides a blueprint for computational design in various research areas beyond solid mechanics, such as polymer chemistry, fluid dynamics, meteorology, and robotics. © 2024 American Association for the Advancement of Science. All rights reserved.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/121382
- DOI
- 10.1126/sciadv.adk4284
- ISSN
- 2375-2548
- Article Type
- Article
- Citation
- Science Advances, vol. 10, no. 5, 2024-02
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- There are no files associated with this item.
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