mat-md-probability-density

You can map ionic diffusion pathways by converting time-sampled fractional coordinates of mobile ions from an ASE .traj file into a volumetric probability density grid. This grid is output as a CHGCAR file for direct isosurface visualization in VESTA.

To visualize conduction pathways in VESTA, generate a CHGCAR-format probability density map from your MD trajectory. The Skill aggregates fractional coordinates of diffusing ions into a structured grid that VESTA reads natively for isosurface rendering.

Yes, you can smooth sparse ionic hopping data from short AIMD trajectories using built-in Gaussian smoothing. The tool also supports logarithmic compression to better reveal continuous diffusion paths that might otherwise appear disjointed.

You can skip equilibration frames by setting the ignore_ps parameter in the execution script. This excludes initial trajectory data before the system stabilizes, ensuring the probability density calculation only includes equilibrated ionic diffusion.

Generating a CHGCAR probability density map requires an ASE .traj trajectory file as input and uses numpy, scipy, and pymatgen libraries for grid computation. The output is a standard CHGCAR volumetric file for visualization.

You select specific diffusing ion species through species selection parameters in the provided script. This filters the MD trajectory fractional coordinates so the probability density grid only aggregates positions of the targeted mobile ions.