Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor.
Publication Date
2022-06-09Journal Title
Sci Data
ISSN
2052-4463
Publisher
Springer Science and Business Media LLC
Volume
9
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Kumar, P., Kabra, S., & Cole, J. M. (2022). Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor.. Sci Data, 9 (1) https://doi.org/10.1038/s41597-022-01301-w
Abstract
The emerging field of material-based data science requires information-rich databases to generate useful results which are currently sparse in the stress engineering domain. To this end, this study uses the'materials-aware' text-mining toolkit, ChemDataExtractor, to auto-generate databases of yield-strength and grain-size values by extracting such information from the literature. The precision of the extracted data is 83.0% for yield strength and 78.8% for grain size. The automatically-extracted data were organised into four databases: a Yield Strength, Grain Size, Engineering-Ready Yield Strength and Combined database. For further validation of the databases, the Combined database was used to plot the Hall-Petch relationship for, the alloy, AZ31, and similar results to the literature were found, demonstrating how one can make use of these automatically-extracted datasets.
Keywords
Databases, Factual, Data Mining
Sponsorship
Royal Academy of Engineering (RCSRF1819\7\10)
RCUK | Science and Technology Facilities Council (STFC) (Fellowship support from ISIS Neutron and Muon Source)
U.S. Department of Energy (DOE) (DE-AC02-06CH11357)
Identifiers
s41597-022-01301-w, 1301
External DOI: https://doi.org/10.1038/s41597-022-01301-w
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337942
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk