GEMDAT: a Python toolkit for site-resolved diffusion analysis in solid-state molecular dynamics

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Molecular dynamics (MD) simulations are crucial for understanding diffusion in solid-state materials.
• Current MD tools often fail to utilize the full potential of trajectory data, limiting analysis to standard metrics.
• This underutilization hinders a comprehensive understanding of atomic-level transport phenomena.

Purpose of the Study:

• Introduce GEMDAT, a Python toolkit for detailed, site-resolved diffusion analysis of MD simulations.
• Enable extraction of advanced diffusion metrics beyond standard calculations.
• Facilitate the connection between atomic-level features and macroscopic transport properties.

Main Methods:

• Developed GEMDAT, a user-friendly Python toolkit for MD trajectory analysis.
• Implemented advanced analysis features including jump rates, activation energies, and rotational diffusion.
• Integrated visualization and caching for efficient and interactive workflows.
• Included automated and manual identification of migration sites.

Main Results:

• GEMDAT extracts site-specific diffusion parameters (jump rates, activation energies, vibrational amplitudes, site occupancies).
• The toolkit provides data directly comparable to experimental diffraction results.
• Demonstrated utility across diverse material systems: crystalline/amorphous ionic conductors, plastic crystals, and surfaces.
• Successfully linked atomic-level structural insights to macroscopic transport properties.

Conclusions:

• GEMDAT significantly enhances the analytical capabilities for MD simulations of solid-state materials.
• The toolkit facilitates a deeper understanding of diffusion mechanisms at the atomic level.
• GEMDAT serves as a valuable tool for guiding the optimization and development of next-generation solid-state materials.