7 research outputs found
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Data Mining Chemistry and Crystal Structure
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.Engineering and Applied Science
Proposed definition of crystal substructure and substructural similarity
There is a clear need for a practical and mathematically rigorous description of local structure in inorganic compounds so that structures and chemistries can be easily compared across large data sets. Here a method for decomposing crystal structures into substructures is given, and a similarity function between those substructures is defined. The similarity function is based on both geometric and chemical similarity. This construction allows for large-scale data mining of substructural properties, and the analysis of substructures and void spaces within crystal structures. The method is validated via the prediction of Li-ion intercalation sites for the oxides. Tested on databases of known Li-ion-containing oxides, the method reproduces all Li-ion sites in an oxide with a maximum of 4 incorrect guesses 80% of the time.National Science Foundation (U.S.) (SI2-SSI Collaborative Research program Award OCI-1147503)United States. Dept. of Energy. Office of Basic Energy Sciences (Grant EDCBEE
Crystal Structure Search with Random Relaxations Using Graph Networks
Materials design enables technologies critical to humanity, including
combating climate change with solar cells and batteries. Many properties of a
material are determined by its atomic crystal structure. However, prediction of
the atomic crystal structure for a given material's chemical formula is a
long-standing grand challenge that remains a barrier in materials design. We
investigate a data-driven approach to accelerating ab initio random structure
search (AIRSS), a state-of-the-art method for crystal structure search. We
build a novel dataset of random structure relaxations of Li-Si battery anode
materials using high-throughput density functional theory calculations. We
train graph neural networks to simulate relaxations of random structures. Our
model is able to find an experimentally verified structure of Li15Si4 it was
not trained on, and has potential for orders of magnitude speedup over AIRSS
when searching large unit cells and searching over multiple chemical
stoichiometries. Surprisingly, we find that data augmentation of adding
Gaussian noise improves both the accuracy and out of domain generalization of
our models.Comment: Removed citations from the abstract, paper content is unchange
Switching Stability: An Examination of the Control Algorithm Used by Team Caltech in the DARPA Grand Challenge 2005
Data-mined similarity function between material compositions
A new method for assessing the similarity of material compositions is described. 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, selecting the correct prototype from a list of known prototypes within five guesses 75% of the time. It performs particularly well on the quaternary oxides, selecting the correct prototype from a list of known prototypes on the first guess 65% of the time.United States. Dept. of Energy (Contract DE-FG02-96ER45571)United States. Office of Naval Research (Contract N00014-11-1-0212)National Science Foundation (U.S.) (Cyber-enabled Discover and Innovation Contract ECCS-0941043