FAQ

• What are the units being used in kinisi?

  After kinisi reads in a file, the units are modified such that distances are in angstrom and time in picoseconds (these are the standard units for length and time in MDAnalysis objects, while for VASP we internally convert from femtoseconds to picoseconds on parsing), this means that time objects in the parser_params should use in the input unit (i.e. femtoseconds VASP objects or picoseconds for MDAnalysis objects). The msd attribute are in units of squared-angstrom and the dt attribute are in units of picoseconds. The diffusion or jump-diffusion coefficient has units of squared-centimetre per second and the conductivity is millisiemens per centimetre (these were chosen as they are common units for these parameters).

• I have been using kinisi in my research and would like to cite the package, how should I do this?

  Thanks for using kinisi. Please cite the JOSS publication, the methodological arXiv preprint, and specifically refer to the version of kinisi that has been used.

• How does kinisi work?

  Please have a look at our arXiv preprint to learn about how kinisi works.

• How does kinisi compare to the similar functionality in pymatgen?

  The kinisi API is based on the pymatgen equivalent. However, kinisi offers insight that is not possible with pymatgen. We investigate this in this Jupyter Notebook.

• I got a strange memory_limit related error, what’s happening?

  Check out the specific page that we have related to this error.

• Running the documentation locally gave me different numbers, how come?

  kinisi aims to be reproducible on a per-environment basis. Therefore, we do not pin versions in the pyproject.toml hence, when you run pip install '.[docs]' you might get different package versions and due to the stochastic nature of the sampling in kinisi, this leads to slightly different values in the results. kinisi allows a random_state to be passed to many methods, however, this will only ensure reproducibility when the same enviroment is present. Consider using pinned versions in a conda/mamba environment if you want to enable true reproducibility.

• How are trajectories unwrapped?

  When calculating displacements, kinisi uses a simple heuristic to unwrap trajectories. If the displacement between two steps, is greater than half the simulation cell length, kinisi wraps that displacement. This scheme assumes that no particle moves more than one cell between steps. Therefore, it requires that enough simulation data is provided to kinisi. This is the reason for not supporting NPT simulations, although this is being investigated.