GEMDAT

Gemdat is a Python library for the analysis of diffusion in solid-state electrolytes from Molecular Dynamics simulations. Gemdat is built on top of Pymatgen, making it straightforward to integrate it into your Pymatgen-based workflows.

With Gemdat, you can:

• Explore your MD simulation via an easy-to-use Python API
• Load and analyze trajectories from VASP, LAMMPS, GROMACS, and ASE simulation data
• Find jumps and transitions between sites
• Effortlessly calculate tracer and jump diffusivity
• Easily perform kinisi diffusion analysis
• Characterize and visualize diffusion pathways
• Plot main structural and diffusion properties

To install:

pip install gemdat

The source code is available from Github.

Suggestions, improvements, and edits are most welcome.

Usage

The following snippet to analyze the diffusion trajectory from VASP data.

from gemdat import Trajectory

trajectory = Trajectory.from_vasprun('../example/vasprun.xml')

trajectory.plot_displacement_per_element()

diff_trajectory = trajectory.filter('Li')

diff_trajectory.plot_msd_per_element()
diff_trajectory.plot_displacement_per_atom()
diff_trajectory.plot_displacement_histogram()
diff_trajectory.plot_frequency_vs_occurence()
diff_trajectory.plot_vibrational_amplitudes()

Characterize transitions and jumps between sites:

from gemdat.io import load_known_material

sites = load_known_material('argyrodite', supercell=(2, 1, 1))

transitions = trajectory.transitions_between_sites(
    sites=sites,
    floating_specie='Li',
)

jumps = transitions.jumps()

jumps.plot_jumps_vs_distance()
jumps.plot_jumps_vs_time()
jumps.plot_collective_jumps()
jumps.plot_jumps_3d()

jumps.jump_diffusivity(dimensions=3)

To calculate different metrics, such as tracer diffusivity:

from gemdat import TrajectoryMetrics

metrics = TrajectoryMetrics(diff_trajectory)

metrics.tracer_diffusivity(dimensions=3)
metrics.haven_ratio(dimensions=3)
metrics.tracer_conductivity(dimensions=3)
metrics.particle_density()
metrics.vibration_amplitude()

Development

Check out our Contributing Guidelines to get started with development.

How to Cite

• A.K. Lavrinenko, T. Famprikis, V. Landgraf, J.R. Heringa, S. Smeets, V. Azizi, S. Ciarella, M. Wagemaker & A. Vasileiadis. GEMDAT: a Python toolkit for site-resolved diffusion analysis in solid-state molecular dynamics. npj Comput Mater (2026). doi: 10.1038/s41524-026-02133-7

Dissemination

• Lorentz workshop 7-11 April (2025): Molecular Dynamics for Materials Science (pdf)

Credits

Some parts of the code in this repository are based on Matlab code to analyse Molecular Dynamics simulations.

For background information on how some of the properties are calculated, check out the accompanying paper:

• Niek J.J. de Klerk, Eveline van der Maas and Marnix Wagemaker, ACS Applied Energy Materials, (2018), doi: 10.1021/acsaem.8b00457