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Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals

Felix A. Faber1, Alexander Lindmaa2, O. Anatole von Lilienfeld1,3,*, and Rickard Armiento2,†

  • 1Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel, 4056 Basel, Switzerland
  • 2Department of Physics, Chemistry and Biology, Linköping University, SE-581 83 Linköping, Sweden
  • 3General Chemistry, Free University of Brussels, Pleinlaan 2, 1050 Brussels, Belgium

  • *anatole.vonlilienfeld@unibas.ch
  • †rickard.armiento@liu.se
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Phys. Rev. Lett. 117, 135502 – Published 20 September, 2016

DOI: https://doi.org/10.1103/PhysRevLett.117.135502

Abstract

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 ∼2×106 pristine ABC2D6 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 10×103 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 2×106 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.

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Phys. Rev. Lett. 117, 135502– Published 20 September, 2016
  • Received 24 August 2015
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