N. A. Katcho, J. Carrete, M. Reynaud, G. Rousse, M. Casas-Cabanas, N. Mingo, J. Rodriguez-Carvajal, J. Carrasco:
"An investigation of the structural properties of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine learning";
Journal of Applied Crystallography, 52 (2019), S. 148 - 157.

Kurzfassung englisch:
Progress in energy-related technologies demands new and improved materials
with high ionic conductivities. Na- and Li-based compounds have high priority in
this regard owing to their importance for batteries. This work presents a highthroughput
exploration of the chemical space for such compounds. The results
suggest that there are significantly fewer Na-based conductors with low
migration energies as compared to Li-based ones. This is traced to the fact that,
in contrast to Li, the low diffusion barriers hinge on unusual values of some
structural properties. Crystal structures are characterized through descriptors
derived from bond-valence theory, graph percolation and geometric analysis. A
machine-learning analysis reveals that the ion migration energy is mainly
determined by the global bottleneck for ion migration, by the coordination
number of the cation and by the volume fraction of the mobile species. This
workflow has been implemented in the open-source Crystallographic Fortran
Modules Library (CrysFML) and the program BondStr. A ranking of Li- and
Na-based ionic compounds with low migration energies is provided.

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