Materials data validation and imputation with an artificial neural network
Materials data validation and imputation with an artificial neural network
复制标题
DOI：
10.1016/j.commatsci.2018.02.002
复制
发表时间：
2018-02
影响因子：
3.3
通讯作者：
P. Verpoort;P. MacDonald;G. Conduit
中科院分区：
文献类型：
--
作者：
P. Verpoort;P. MacDonald;G. Conduit
我们应用人工神经网络来模拟和验证材料的性能。神经网络算法在训练和预测中都具有处理不完整数据集的独特能力，因此它可以将属性视为输入，允许它利用组合属性和属性属性相关性来提高预测的质量，并且还可以将图形数据作为单个实体处理。该框架通过不同的验证方案进行了测试，然后应用于合金和聚合物的材料案例研究。该算法在一个商业材料数据库中发现了20个错误，并与原始数据源进行了确认。
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.