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WW1.03 - Similarity Functions for Datamining Compositional and Structural Relations Between Materials 
April 22, 2014   9:00am - 9:30am

The availability of large amounts of data generated by high-throughput computing or experimenting has generated interested in the application of machine learning techniques to materials science [1]. Machine learning of materials behavior requires the use of feature vectors or descriptors that capture the essential compositional or structural information that is most likely to influence a property. We will present a new method for assessing the similarity of material compositions. A similarity measure is important for the classification and clustering of compositions. The similarity of the material compositions is calculated utilizing a data-mined ionic substitutional similarity based upon the probability with which two ions will substitute for each other within the same structure prototype. The method is validated via the prediction of crystal structure prototypes for oxides from the Inorganic Crystal Structure Database. It performs particularly well on the quaternary oxides, predicting the correct prototype within 5 guesses 90% of the time. We expect that this compositional similarity measure can be used to classify other properties as well.[1] C. Fischer et al, Predicting Crystal Structure: Merging Data Mining with Quantum Mechanics, Nature Materials, 5 (8), pp. 641-6 (2006). G. Hautier et al, Data Mined Ionic Substitutions for the Discovery of New Compounds, Inorganic Chemistry, 50 (2), 656-663 (2011).

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