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dc.contributor.authorStrubell, Emma
dc.contributor.authorSaunders, Adam
dc.contributor.authorMcCallum, Andrew
dc.contributor.authorOlivetti, Elsa
dc.contributor.authorKim, Edward
dc.contributor.authorHuang, Kevin Joon-Ming
dc.contributor.authorTomala, Alex
dc.contributor.authorMatthews, Sara C.
dc.date.accessioned2018-06-15T15:53:35Z
dc.date.available2018-06-15T15:53:35Z
dc.date.issued2017-09
dc.date.submitted2017-04
dc.identifier.issn2052-4463
dc.identifier.urihttp://hdl.handle.net/1721.1/116340
dc.description.abstractPredictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.en_US
dc.publisherNature Publishing Groupen_US
dc.relation.isversionofhttp://dx.doi.org/10.1038/SDATA.2017.127en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceScientific Dataen_US
dc.titleMachine-learned and codified synthesis parameters of oxide materialsen_US
dc.typeArticleen_US
dc.identifier.citationKim, Edward et al. “Machine-Learned and Codified Synthesis Parameters of Oxide Materials.” Scientific Data 4 (September 2017): 170127en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineering
dc.contributor.mitauthorKim, Edward
dc.contributor.mitauthorHuang, Kevin Joon-Ming
dc.contributor.mitauthorTomala, Alex
dc.contributor.mitauthorMatthews, Sara C.
dc.relation.journalScientific Dataen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-05-11T12:53:00Z
dspace.orderedauthorsKim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsaen_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_CCen_US


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