kinisi: Bayesian analysis of mass transport from molecular dynamics simulations

Andrew Mccluskey, Alexander Squires, Samuel Coles, Benjamin Morgan

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Abstract

kinisi is a Python package for estimating transport coefficients—e.g., self-diffusion coefficients, D*—and their corresponding uncertainties from molecular dynamics simulation data: it includes an implementation of the approximate Bayesian regression scheme described in (McCluskey et al., 2023), wherein the mean-squared displacement (MSD) of mobile atoms is modelled as a multivariate normal distribution that is parametrised from the input simulation data. kinisi uses Markov-chain Monte Carlo (Foreman-Mackey et al., 2019; Goodman & Weare, 2010) to sample this model multivariate normal distribution to give a posterior distribution of linear model ensemble MSDs that are compatible with the observed simulation data. For each linear ensemble MSD, x(t), a corresponding estimate of the diffusion coefficient, D*, is given via the Einstein relation. The posterior distribution of compatible model ensemble MSDs calculated by kinisi gives a point estimate for the most probable value of D*, given the observed simulation data, and an estimate of the corresponding uncertainty in D*. A detailed description of the numerical method used in kinisi is given in (McCluskey et al., 2023). kinisi also provides equivalent functionality for estimating collective transport coefficients, i.e., jump-diffusion coefficients and ionic conductivities.
Original languageEnglish
JournalThe Journal of Open Source Software
Publication statusSubmitted - 17 Aug 2023

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