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Topological coordination numbers and coordination reciprocity from electron-density distributions
Frank R Wagner1, Riccardo Freccero1, Yuri Grin1
1Max-Planck-Institut für Chemische Physik fester Stoffe, Dresden, Germany.
A new topological coordination number (tCN) method refines atomic coordination analysis by considering atomic sizes and electron density. This approach offers a more precise characterization of complex crystal structures, aiding AI applications.
Area of Science:
- Quantum chemistry
- Solid-state physics
- Crystallography
Background:
- Traditional coordination number calculations often overlook atomic size effects and electron density distribution.
- Existing methods like Voronoi-Dirichlet partitioning (VDP) may not fully capture nuanced coordination scenarios in complex structures.
Purpose of the Study:
- To develop a generalized topological effective coordination number (tCN) approach.
- To incorporate coordination reciprocity and geometrical weighting for chemically meaningful coordination numbers.
- To provide a more accurate method for analyzing atomic coordination in diverse crystal structures.
Main Methods:
- Utilizing triangulated surface data sets from the Quantum Theory of Atoms in Molecules (QTAIM) interatomic surfaces.
- Calculating solid angles subtended at nuclear positions by diatomic contact surfaces.
- Developing weighting functions based on geometrical properties of areas to rank sub-coordination scenarios.
Main Results:
- The tCN approach naturally includes the effect of varying atomic sizes, unlike VDP.
- Differences between VDP and tCN results were observed even in highly symmetrical element structures due to electron density decay.
- The method effectively ranks multiple sub-coordination scenarios in complex structures like TiNiSi type compounds, providing relative weights.
Conclusions:
- The tCN method offers a more precise and comprehensive characterization of atomic coordination compared to VDP.
- It is particularly advantageous for analyzing complex intermetallic phases and provides valuable input for AI-driven structure-property relationship studies.
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