Uncertainty quantification in diffusion

kinisi is an open-source Python package that can accurately estimate diffusion processes in atomic and molecular systems and determine an accurate estimate of the uncertainty in these processes.

This is achieved by modelling the diffusion process as a multivariate normal distribution, based on that for a random walker. This ensures and accurate estimation of the diffusion coefficient and it’s uncertainty. More information about the approach kinisi uses can be found in the methodology preprint article, which is also introduced in this poster.

kinisi can handle simulation trajectories from many common molecular dynamics packages, including VASP and those that can be read by MDAnalysis. Examples of some of these analyses are shown in the notebooks, also given there is an example of using kinisi to investigate the Arrhenius relationship of diffusion as a function of temperature.

An example of the output from kinisi; showing the determined mean-squared displacements (solid line),
the estimated Einstein diffusion relationship (blue regions representing descreasing credible intervals).

Contributors

Andrew R. McCluskey | Benjamin J. Morgan | Alex G. Squires | Josh Dunn