Hi everyone,
Tomorrow Lusann Wren Yang will be our guest speaker during our regular
group meeting. The title and abstract of her talk are below.
Best,
Felipe Herrera
*Title: *Data Mining Chemistry and Crystal Structure
*Abstract:*
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.
The similarity functions between ions, compositions, substructures and
crystal structure are based upon a data-mined probability with which two
ions will substitute for each other within the same structure prototype.
The composition similarity 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. The
sustructural similarity is validated via the prediction of Li insertion
sites in the oxides; it finds all of the Li sites with less than 8
incorrect guesses 90% of the time.
Show replies by date