MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Using Deep Learning to Predict Fracture Patterns in Crystalline Solids

Author(s)
Hsu, Yu-Chuan; Yu, Chi-Hua; Buehler, Markus J
Thumbnail
DownloadAccepted version (3.877Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/
Metadata
Show full item record
Abstract
Fracture is a catastrophic and complex process that involves various time and length scales. Scientists have devoted vast efforts toward understanding the underlying mechanisms for centuries, with much work left in terms of predictability of models and fundamental understanding. To this end, we present a machine-learning approach to predict fracture processes connecting molecular simulation into a physics-based artificial intelligence (AI) multiscale model. Our model exhibits predictive power not only regarding the computed fracture patterns but also for fracture toughness—the resistance of cracks to grow. The novel AI-based fracture predictor can also deal with complex loading conditions, here examined for both mode I (tensile) and mode II (shear). These results underscore the excellent predictive power of our model. Potential applications include the design of novel types of high-performance materials, composites design, surface coatings, or innovative bio-inspired structures.
Date issued
2020-05
URI
https://hdl.handle.net/1721.1/132724
Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics; Massachusetts Institute of Technology. Department of Civil and Environmental Engineering; Massachusetts Institute of Technology. Center for Computational Science and Engineering
Journal
Matter
Publisher
Elsevier BV
Citation
Yu-Chuan Hsu, Chi-Hua Yu, Markus J. Buehler, Using Deep Learning to Predict Fracture Patterns in Crystalline Solids, Matter, Volume 3, Issue 1, 2020 © 2020 Elsevier Inc.
Version: Author's final manuscript
ISSN
2590-2385

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.