Abstract
The prediction of the lattice constant of binary body centered cubic crystals is performed in terms of first principle calculations and machine learning. In particular, 1541 binary body centered cubic crystals are calculated using density functional theory. Results from first principle calculations, corresponding information from periodic table, and mathematically tailored data are stored as a dataset. Data mining reveals seven descriptors which are key to determining the lattice constant where the contribution of descriptors is also discussed and visualized. Support vector regression (SVR) technique is implemented to train the data where the predicted lattice constants have the mean score of 83.6% accuracy via crossvalidation and maximum error of 4% when compared to experimentally determined lattice constants. In addition, trained SVR is successful in predicting material combinations from a desired lattice constant. Thus, a set of descriptors for determining the lattice constant is identified and can be used as a base descriptor for lattice constants of further complex crystals. This would allow for the acceleration of the search for lattice constants of desired atomic compositions as well as the prediction of new materials based on a specified lattice constant.
Original language  English 

Article number  204104 
Journal  Journal of Chemical Physics 
Volume  146 
Issue number  20 
DOIs  
Publication status  Published  28 May 2017 
ASJC Scopus subject areas
 Physics and Astronomy(all)
 Physical and Theoretical Chemistry
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Equipment

High Performance Computing (HPC) Facility
Steven Chapman (Manager)
University of BathFacility/equipment: Facility