MDTransport: A Modular, Open-Source, Extensible Python Tool for Computing Transport Properties from GROMACS and...

Discovering novel materials for energy-efficient separations, electrochemical reactions, and energy storage requires characterization of thermophysical properties such as diffusivity and ionic conductivity. Over the past several decades, molecular dynamics (MD) simulations have been playing an important role for predictions of properties in a materials-discovery pipeline. In a typical property estimation workflow, trajectories of materials/fluids of interest are generated using MD simulation engines such as GROMACS or LAMMPS, which are then processed to calculate properties such as molar volume, self-diffusion coefficients, ionic conductivity, radial distribution functions, etc. Several postprocessing tools have been developed to facilitate various property calculations. However, many of these tools are geared toward analyzing trajectories produced from a specific MD engine, requiring expertise in multiple postprocessing tools if more than one MD engine is employed, which is very likely given the unique functionalities offered by various MD engines. In addition, these tools are typically limited to routinely computed properties from MD trajectories and do not readily support more advanced analyses, such as cage correlation lifetimes, elucidation of transport mechanisms, or decomposition of ionic conductivity into self- and cross-correlation contributions. To address this research gap, we introduce MDTransport, a comprehensive, open-source, Python-based postprocessing tool, optimized for the estimation of ionic conductivity, self-diffusion coefficient, Onsager transport coefficient, and transference number. It also calculates ion-ion correlation, performs spatial decomposition of cross-correlations, estimates pair and cage correlation lifetimes, performs cluster analysis, and predicts ion-transport mechanisms, thereby providing insights into the structure of the electrolyte and underlying ion dynamics. An intuitive, user-friendly, modular, plug-and-play design, with a prompt-based command-line interface, also allows customization of various input parameters, aiming to serve researchers from diverse research backgrounds and a wide range of programming expertise.