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Publication Detail
Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Zhang YM, Evans JRG, Yang SF
  • Publisher:
    TAYLOR & FRANCIS LTD
  • Publication date:
    2010
  • Pagination:
    4453, 4474
  • Journal:
    PHILOS MAG
  • Volume:
    90
  • Issue:
    33
  • Print ISSN:
    1478-6435
  • PII:
    927164076
  • Language:
    EN
  • Keywords:
    neural networks, databases, error detection, outlier detection, properties correlations, MATERIALS SCIENCE, TITANIUM-ALLOYS, PREDICTION, DESIGN, VAPORIZATION, HYDROCARBONS, CHEMISTRY, PHASE
  • Addresses:
    Yang, SF
    Queen Mary Univ London
    Dept Mat
    London
    E1 4NS
    England
Abstract
The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.
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