Materials data validation and imputation with an artificial neural network

[Display omitted] 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...

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Bibliographic Details
Published in:Computational materials science 2018-05, Vol.147, p.176-185
Main Authors: Verpoort, P.C., MacDonald, P., Conduit, G.J.
Format: Article
Language:English
Subjects:
Alloys
Materials data
Neural network
Polymers
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Summary:[Display omitted] 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.
ISSN:0927-0256
1879-0801
DOI:10.1016/j.commatsci.2018.02.002