GEMDAT: A Python Toolkit for Site-Resolved Diffusion Analysis in Solid-State Molecular Dynamics

Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials, including ionic conductors, fuel cells, and gas sensors. Most existing studies and software packages, however, extract only basic information—tracer diffusivity and activation energies, usually assuming Arrhenius behaviour. But MD trajectories contain far more information than these standard metrics reveal. To unlock this hidden potential, we introduce GEMDAT—a user-friendly Python-based analysis tool designed to extract detailed diffusional properties from MD simulations of solid-state materials (https://github.com/GEMDAT-repos/GEMDAT.git). GEMDAT allows users to analyse the environment around atomic sites where species migration occurs and examine discrete jump events. The user can define these sites manually or allow GEMDAT to automatically identify them from MD simulation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies by mobile species–quantities that are also valuable for interpreting experimental diffraction data. In addition to basic properties such as mean-square displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT calculates a wide range of diffusion-related properties, including jump rates, attempt frequencies, site-specific activation energies, collective motion, and rotational diffusion. To make the analysis workflow more efficient, GEMDAT caches data after the initial run, speeding up subsequent analyses, and generates visualizations for rapid interpretation of results. We demonstrate the application of GEMDAT on several case studies involving Li- and Na-ion conductors and plastic crystals. These examples showcase how the code extracts atomic-level structural features and connects them to macroscopic transport properties, guiding the optimization and development of solid-state materials.