Auto-generated database of semiconductor band gaps using ChemDataExtractor.

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 pars

SUBMITTER: Dong Q 

PROVIDER: S-EPMC9065101 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature