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dc.contributor.authorVenugopal, Vineeth
dc.date.accessioned2023-12-19T15:06:55Z
dc.date.available2023-12-19T15:06:55Z
dc.date.issued2023-12-06
dc.identifier.urihttps://hdl.handle.net/1721.1/153204
dc.description.abstractAlloy design can potentially benefit from artificial intelligence (AI). Within the broader context of advanced materials design, researchers have explored the creation of various alloys, including ferrous, high-entropy materials, and nonferrous metallic compositions, employing data-driven methodologies. However, a significant hurdle in these endeavors has been the scarcity of extensive, machine-readable databases suitable for training AI models. To address this challenge, researchers have turned to text mining and autonomous data extraction from literature sources. However, a major challenge with this approach is that the essential materials properties and features are not necessarily known, and must be obtained from textual sources to enhance the predictive accuracy of these models.en_US
dc.publisherSpringer International Publishingen_US
dc.relation.isversionofhttps://doi.org/10.1557/s43577-023-00633-1en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer International Publishingen_US
dc.titleAI assists in design of corrosion-resistant alloysen_US
dc.typeArticleen_US
dc.identifier.citationVenugopal, Vineeth. 2023. "AI assists in design of corrosion-resistant alloys." MRS Bulletin, 48.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineering
dc.relation.journalMRS Bulletinen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-12-19T04:39:12Z
dc.language.rfc3066en
dc.rights.holderThe Author(s), under exclusive License to the Materials Research Society
dspace.embargo.termsY
dspace.date.submission2023-12-19T04:39:11Z
mit.journal.volume48en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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