dc.contributor.author | Strubell, Emma | |
dc.contributor.author | Saunders, Adam | |
dc.contributor.author | McCallum, Andrew | |
dc.contributor.author | Olivetti, Elsa | |
dc.contributor.author | Kim, Edward | |
dc.contributor.author | Huang, Kevin Joon-Ming | |
dc.contributor.author | Tomala, Alex | |
dc.contributor.author | Matthews, Sara C. | |
dc.date.accessioned | 2018-06-15T15:53:35Z | |
dc.date.available | 2018-06-15T15:53:35Z | |
dc.date.issued | 2017-09 | |
dc.date.submitted | 2017-04 | |
dc.identifier.issn | 2052-4463 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/116340 | |
dc.description.abstract | Predictive 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.publisher | Nature Publishing Group | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1038/SDATA.2017.127 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Scientific Data | en_US |
dc.title | Machine-learned and codified synthesis parameters of oxide materials | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Kim, Edward et al. “Machine-Learned and Codified Synthesis Parameters of Oxide Materials.” Scientific Data 4 (September 2017): 170127 | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Materials Science and Engineering | |
dc.contributor.mitauthor | Kim, Edward | |
dc.contributor.mitauthor | Huang, Kevin Joon-Ming | |
dc.contributor.mitauthor | Tomala, Alex | |
dc.contributor.mitauthor | Matthews, Sara C. | |
dc.relation.journal | Scientific Data | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2018-05-11T12:53:00Z | |
dspace.orderedauthors | Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa | en_US |
dspace.embargo.terms | N | en_US |
mit.license | PUBLISHER_CC | en_US |