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<p>Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials such as ionic conductors, fuel cells, and gas sensors, yet most existing studies and software tools extract only standard metrics, leaving much of the information contained in the trajectories unused. Here we introduce GEMDAT, a user-friendly Python toolkit for site-resolved diffusion analysis of MD simulations of solid-state materials (<a href="https://github.com/GEMDAT-repos/GEMDATt">https://github.com/GEMDAT-repos/GEMDAT</a>). Beyond mean-squared displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT provides jump rates, attempt frequencies, site-specific activation energies, rotational diffusion, and percolation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies—quantities that are also directly comparable to experimental diffraction data. Migration sites can be defined manually or identified automatically from the trajectory. A built-in caching approach, together with rapid visualization capabilities, makes the workflow fast and interactive. We demonstrate GEMDAT on a series of case studies involving crystalline Li- and Na-ion conductors, plastic crystals, amorphous structures, and surface configurations, showing how the code extracts atomic-level structural features and connects them to macroscopic transport properties, thereby guiding the optimization and development of solid-state materials.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Anastasia K. Lavrinenko,
  • Theodosios Famprikis,
  • Victor Landgraf,
  • Jouke R. Heringa,
  • Stef Smeets,
  • Victor Azizi,
  • Simone Ciarella,
  • Marnix Wagemaker,
  • Alexandros Vasileiadis

摘要

Molecular dynamics (MD) simulations have become essential for understanding diffusion mechanisms in solid-state materials such as ionic conductors, fuel cells, and gas sensors, yet most existing studies and software tools extract only standard metrics, leaving much of the information contained in the trajectories unused. Here we introduce GEMDAT, a user-friendly Python toolkit for site-resolved diffusion analysis of MD simulations of solid-state materials (https://github.com/GEMDAT-repos/GEMDAT). Beyond mean-squared displacements, radial distribution functions, and Arrhenius-based activation energies, GEMDAT provides jump rates, attempt frequencies, site-specific activation energies, rotational diffusion, and percolation. Our tool provides access to vibrational amplitudes, site geometries, and site occupancies—quantities that are also directly comparable to experimental diffraction data. Migration sites can be defined manually or identified automatically from the trajectory. A built-in caching approach, together with rapid visualization capabilities, makes the workflow fast and interactive. We demonstrate GEMDAT on a series of case studies involving crystalline Li- and Na-ion conductors, plastic crystals, amorphous structures, and surface configurations, showing how the code extracts atomic-level structural features and connects them to macroscopic transport properties, thereby guiding the optimization and development of solid-state materials.