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dc.contributor.authorChen, Chun-Teh
dc.contributor.authorRichmond, Deon J.
dc.contributor.authorBuehler, Markus J.
dc.contributor.authorGu, Grace Xiang
dc.date.accessioned2018-08-22T19:10:30Z
dc.date.available2018-08-22T19:10:30Z
dc.date.issued2018-07
dc.date.submitted2018-06
dc.identifier.issn2051-6347
dc.identifier.issn2051-6355
dc.identifier.urihttp://hdl.handle.net/1721.1/117490
dc.description.abstractBiomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant ONR N000141612333)en_US
dc.language.isoen_US
dc.relation.isversionofhttp://dx.doi.org/10.1039/C8MH00653Aen_US
dc.rightsCreative Commons Attribution 3.0 Unported licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.sourceRoyal Society of Chemistryen_US
dc.titleBioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experimenten_US
dc.typeArticleen_US
dc.identifier.citationGu, Grace X. et al. “Bioinspired Hierarchical Composite Design Using Machine Learning: Simulation, Additive Manufacturing, and Experiment.” Materials Horizons (2018)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorGu, Grace Xiang
dc.relation.journalMaterials Horizonsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGu, Grace X.; Chen, Chun-Teh; Richmond, Deon J.; Buehler, Markus J.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8178-6492
mit.licensePUBLISHER_CCen_US


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