Repository logo
Log in(current)
Repository logoMIT Open ScholarshipDSpace@MIT
  1. Home
  2. MIT Open Access Articles
  3. MIT Open Access Articles
  4. Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs

Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs

Thumbnail Image
Download
Name

sciadv.adk4284.pdf

Description
Published version
Size

5.73 MB

Format

Adobe PDF

Checksum (MD5)

6f40d763600b5f63289e8539268d9bd7

sword-2024-11-26T15:50:34.original.xml (130 B)
Original SWORD entry document
Author(s)
Li, Beichen
•
Deng, Bolei
•
Shou, Wan
•
Oh, Tae-Hyun
•
Hu, Yuanming
•
Luo, Yiyue
•
Shi, Liang
•
Matusik, Wojciech
Date Issued
February 2, 2024
Journal
Science Advances
Publisher
American Association for the Advancement of Science
Citation
Beichen Li et al. ,Computational discovery of microstructured composites with optimal stiffness-toughness trade-offs.Sci. Adv.10,eadk4284(2024).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
http://creativecommons.org/licenses/by-nc/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/157681
DOI of Published Version
https://doi.org/10.1126/sciadv.adk4284
Repository logo
PrivacyPermissionsAccessibilityContact us
Repository logo
Notify us about copyright concerns.