Output details
15 - General Engineering
University of Southampton
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Output 0 of 0 in the submission
Article title
Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations
Type
D - Journal article
Title of journal
Philosophical Magazine
Article number
-
Volume number
90
Issue number
33
First page of article
4453
ISSN of journal
1478-6435
Year of publication
2010
Number of additional authors
2
Additional information
Significance of output:
During the search for property-property correlations in materials using artificial intelligence rather than traditional composition-structure-property mapping in materials science, we found artificial neural networks are also capable of checking scientific data in handbooks and databases, using hidden property-property correlations. Errors were found in widely used handbooks and databases even for properties of the elements. Manually checking of such big data sets is a very difficult job. This offers a possible automated data-checking approach that can be used in database cleansing.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
