Auto-generated database of semiconductor band gaps using ChemDataExtractor.
Publication Date
2022-05-03Journal Title
Sci Data
ISSN
2052-4463
Publisher
Nature Publishing Group UK
Volume
9
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Dong, Q., & Cole, J. (2022). Auto-generated database of semiconductor band gaps using ChemDataExtractor.. Sci Data, 9 (1) https://doi.org/10.1038/s41597-022-01294-6
Abstract
Large-scale databases of band gap information about semiconductors that are curated from the scientific literature have significant usefulness for computational databases and general semiconductor materials research. This work presents an auto-generated database of 100,236 semiconductor band gap records, extracted from 128,776 journal articles with their associated temperature information. The database was produced using ChemDataExtractor version 2.0, a 'chemistry-aware' software toolkit that uses Natural Language Processing (NLP) and machine-learning methods to extract chemical data from scientific documents. The modified Snowball algorithm of ChemDataExtractor has been extended to incorporate nested models, optimized by hyperparameter analysis, and used together with the default NLP parsers to achieve optimal quality of the database. Evaluation of the database shows a weighted precision of 84% and a weighted recall of 65%. To the best of our knowledge, this is the largest open-source non-computational band gap database to date. Database records are available in CSV, JSON, and MongoDB formats, which are machine readable and can assist data mining and semiconductor materials discovery.
Keywords
Data Descriptor, /639/766/1130/2798, /639/766/119/1000, /639/301/1005/1007, /639/301/119/1000, data-descriptor
Identifiers
s41597-022-01294-6, 1294
External DOI: https://doi.org/10.1038/s41597-022-01294-6
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336828
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.