Materials data validation and imputation with an artificial neural network

P. C. Verpoort; P. MacDonald; G. J. Conduit

doi:10.1016/j.commatsci.2018.02.002

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arXiv · 1803.00133

Materials data validation and imputation with an artificial neural network

P. C. Verpoort·P. MacDonald·G. J. Conduit

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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.

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BibTeX RIS

P. C. Verpoort, P. MacDonald, G. J. Conduit. 2018-02-28. Materials data validation and imputation with an artificial neural network. https://doi.org/10.1016/j.commatsci.2018.02.002

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