  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.
