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.
@article{arxiv.1803.00133,
title = {Materials data validation and imputation with an artificial neural network},
author = {P. C. Verpoort and P. MacDonald and G. J. Conduit},
journal= {arXiv preprint arXiv:1803.00133},
year = {2018}
}