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CAVD, towards better characterization of void space for ionic transport analysis.
Bing He1, Anjiang Ye1, Shuting Chi1
1School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
This study enhances crystal structure analysis for ionic transport by incorporating Voronoi faces, improving the identification of mobile ion pathways in materials. The new method, implemented in the CAVD package, accurately maps ion sites for machine learning applications.
Area of Science:
- Materials Science
- Crystallography
- Computational Chemistry
Background:
- Voronoi tessellation is used for crystal structure analysis.
- Current methods map local voids using Voronoi vertices and edges.
- Mobile ions can be located on Voronoi faces, missed by standard approaches.
Purpose of the Study:
- To extend Voronoi tessellation to include faces for improved ionic transport analysis.
- To develop a robust method for mapping mobile ion pathways in crystal structures.
- To enable better material discovery and machine learning applications.
Main Methods:
- Developed an extended Voronoi tessellation method including faces.
- Implemented the method in the CAVD Python package.
- Validated the approach on 6,955 ionic compounds from the ICSD.
Main Results:
- Achieved a 99% recovery rate for mobile ion lattice sites.
- Successfully identified ion pathways missed by standard Voronoi methods.
- Generated quantitative network descriptors for material ranking.
Conclusions:
- The extended Voronoi method accurately maps mobile ion sites, including those on faces.
- The CAVD package provides a powerful tool for analyzing ionic transport in materials.
- The generated descriptors facilitate materials discovery and machine learning.
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