Auto-generating databases of Yield Strength and Grain Size using ChemDataExtractor

Journal Article · 09 June 2022 · Scientific Data

DOI: https://doi.org/10.1038/s41597-022-01301-w · OSTI ID:1871768

Kumar, Pankaj; Kabra, Saurabh; Cole, Jacqueline M.

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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States). Argonne Leadership Computing Facility (ALCF)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1871768
• Journal Information: Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 9; ISSN 2052-4463
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United Kingdom
• Language: English