Materials data validation and imputation with an artificial neural network
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Verpoort, PC
MacDonald, P
Conduit, GJ
Abstract
We apply an artificial neural network to model and verify material properties. The neural network algorithm has a unique capability to handle incomplete data sets in both training and predicting, so it can regard properties as inputs allowing it to exploit both composition-property and property-property correlations to enhance the quality of predictions, and can also handle a graphical data as a single entity. The framework is tested with different validation schemes, and then applied to materials case studies of alloys and polymers. The algorithm found twenty errors in a commercial materials database that were confirmed against primary data sources.
Description
Keywords
Materials data, Neural network, Alloys, Polymers
Journal Title
Computational Materials Science
Conference Name
Journal ISSN
0927-0256
1879-0801
1879-0801
Volume Title
147
Publisher
Elsevier BV
Publisher DOI
Sponsorship
The Royal Society (uf130122)