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dc.contributor.authorBuehler, Markus J.
dc.date.accessioned2022-04-19T12:50:43Z
dc.date.available2022-04-19T12:50:43Z
dc.date.issued2022-03-12
dc.identifier.urihttps://hdl.handle.net/1721.1/141916
dc.description.abstractAbstract The spontaneous assembly of materials from elementary building blocks is one of the most intriguing natural phenomena. Conventional modeling relies physical approaches to examine such processes. In this paper, a framework is proposed to offer an alternative paradigm, via the use of deep learning, and specifically the use of generative adversarial models as well as a combination of natural language processing and transformer neural nets to create hierarchical assemblies of building blocks. We study the assembly of elementary flame particles into hierarchical materials with features across scales, illustrating the Universality–Diversity Principle (UDP), and create novel material using additive manufacturing. Graphical abstracten_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttps://doi.org/10.1557/s43579-022-00171-yen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceSpringer International Publishingen_US
dc.titleDeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structuresen_US
dc.typeArticleen_US
dc.identifier.citationBuehler, Markus J. 2022. "DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures."
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-04-16T03:27:38Z
dc.language.rfc3066en
dc.rights.holderThe Author(s), under exclusive licence to The Materials Research Society
dspace.embargo.termsY
dspace.date.submission2022-04-16T03:27:36Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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