Machine Learning Energies of 2 Million Elpasolite (ABC(2)D(6)) Crystals

Faber, Felix A. and Lindmaa, Alexander and von Lilienfeld, O. Anatole and Armiento, Rickard. (2016) Machine Learning Energies of 2 Million Elpasolite (ABC(2)D(6)) Crystals. Physical Review Letters, 117 (13). p. 135502.

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Official URL: http://edoc.unibas.ch/53279/

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Elpasolite is the predominant quaternary crystal structure (AlNaK2F6 prototype) reported in the Inorganic Crystal Structure Database. We develop a machine learning model to calculate density functional theory quality formation energies of all similar to 2 x 10(6) pristine ABC(2)D(6) elpasolite crystals that can be made up from main-group elements (up to bismuth). Our model`s accuracy can be improved systematically, reaching a mean absolute error of 0.1 eV/atom for a training set consisting of 10 x 10(3) crystals. Important bonding trends are revealed: fluoride is best suited to fit the coordination of the D site, which lowers the formation energy whereas the opposite is found for carbon. The bonding contribution of the elements A and B is very small on average. Low formation energies result from A and B being late elements from group II, C being a late (group I) element, and D being fluoride. Out of 2 x 10(6) crystals, 90 unique structures are predicted to be on the convex hull-among which is NFAl2Ca6, with a peculiar stoichiometry and a negative atomic oxidation state for Al.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Physical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:25 Jan 2017 15:00
Deposited On:25 Jan 2017 15:00

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