The availability of large amounts of data generated by high-throughput computing and experimentation has generated interest in the application of machine learning techniques to materials science. Machine learning of materials behavior requires the use of feature vectors that capture compositional or structural information influence a target property. We present methods for assessing the similarity of compositions, substructures, and crystal structures. Similarity measures are important for the classification and clustering of data points, allowing for the organization of data and the prediction of materials properties.
