GEMDAT

Studying materials at a molecular nanoscale level provides the critical in-depth understanding required for enabling next-generation applications. Molecular dynamics (MD) is a crucial technique that delivers predictive guidance for novel material discovery and optimization, especially considering the rise of solid-state batteries, consisting of, perfectly suitable for this technique, superionic electrolytes. There are various crucial physical properties that we can extract from a single molecular dynamics simulation, including vibrational amplitudes, attempt frequencies, site occupations, jump rates, activation energies, collective jumps, radial distribution functions, and atom displacements. Knowing the properties mentioned above will enable researchers to understand complicated diffusion processes and unravel limiting factors while, at the same time, concrete directions for material improvement will be more apparent and easily understood. However, most literature, and the freely available software options, commonly report only the tracer diffusivity and the resulting activation energy barrier calculated from Arrhenius law. We have developed a robust code that, on top of tracer diffusivity, can extract all the relevant quantities, making them readily available to the researcher. Our code is based on MATLAB and works with non-flexible output files from MD simulations. This proposal aims to transform the existing code into a modern python-based, open-access analysis tool for MD. The new tool will be able to capture, report and plot all the relevant parameters, giving an edge on analysis and intelligent visualization. Combined with user-friendliness, this tool is expected to play a crucial role for the scientific community in unraveling material properties in various fields and applications.