Computer simulations serve as a prevalent approach for investigating the dynamic behavior of fluids. LAMMPS and GROMACS are among the most widely used molecular dynamics (MD) simulation packages, each offering distinct advantages that contribute to their widespread adoption in the scientific community. To address these challenges, the availability of validated, open-source postprocessing tools for trajectory analysis and transport property calculations is essential, as they enable researchers to conduct molecular simulations with greater reliability and reproducibility. Several software packages, including TRAVIS, StreaMD, nMOLDYN, LiquidLib, MDAnalysis, and MDTraj, are commonly used for post-processing MD simulations. However, these tools have limitations in computing transport properties. Existing tools, such as PyLAT, primarily focus on analyzing outputs from LAMMPS simulations, but its capabilities for ionic conductivity calculations remain constrained. Specifically, PyLAT predominantly utilizes the Green-Kubo formalism, which requires accurate autocorrelation functions and is highly sensitive to the ensemble used in simulations. In contrast, the Einstein relation provides a more robust and computationally efficient alternative for calculating ionic conductivity. Herein, we present a Python-based post-processing tool developed to facilitate high-throughput transport analysis of simulation outputs from both GROMACS and LAMMPS and enhance the reliability of computed results. It enables users to compute transport properties such as self-diffusion coefficients, ionic conductivity using Nernst-Einstein and Einstein formalisms, transference numbers, ion-ion correlations, and spatial decomposition analysis of cross-correlations using established best-practice methodologies.
