Journal
JOURNAL OF APPLIED CRYSTALLOGRAPHY
Volume 52, Issue -, Pages 148-157Publisher
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600576718018484
Keywords
bond-valence theory; machine learning; high throughput; Li; Na-ion conductors
Categories
Funding
- Ministerio de Economia y Competitividad (MINECO) of the Spanish Government through the Proyectos I + D Retos Retos 2016 program (ION-STORE) [ENE2016-81020-R]
- ANR through project Carnot MAPPE
Ask authors/readers for more resources
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 high-throughput 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available