Exploiting redundancy in large materials datasets for efficient machine learning with less data.
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| Title: | Exploiting redundancy in large materials datasets for efficient machine learning with less data. |
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| Authors: | Li K; Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada., Persaud D; Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada., Choudhary K; Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, USA., DeCost B; Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, Gaithersburg, MD, USA., Greenwood M; Canmet MATERIALS, Natural Resources Canada, 183 Longwood Road south, Hamilton, ON, Canada., Hattrick-Simpers J; Department of Materials Science and Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.; Acceleration Consortium, University of Toronto, 27 King's College Cir, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.; Vector Institute for Artificial Intelligence, 661 University Ave, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca.; Schwartz Reisman Institute for Technology and Society, 101 College St, Toronto, ON, Canada. jason.hattrick.simpers@utoronto.ca. |
| Source: | Nature communications [Nat Commun] 2023 Nov 10; Vol. 14 (1), pp. 7283. Date of Electronic Publication: 2023 Nov 10. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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