Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor.
Springer Science and Business Media LLC
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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
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.
Data Descriptor, /639/166/988, /639/301/1023/1026, /639/301/1023/303, data-descriptor
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)
External DOI: https://doi.org/10.1038/s41597-022-01301-w
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337942