Materials data validation and imputation with an artificial neural network
Computational Materials Science
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Verpoort, P., MacDonald, P., & Conduit, G. (2018). Materials data validation and imputation with an artificial neural network. Computational Materials Science, 147 176-185. https://doi.org/10.1016/j.commatsci.2018.02.002
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.
The Royal Society (uf130122)
External DOI: https://doi.org/10.1016/j.commatsci.2018.02.002
This record's URL: https://www.repository.cam.ac.uk/handle/1810/275813