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Abstract
kinisi is a Python package for estimating transport coefficients—e.g., selfdiffusion 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 meansquared displacement (MSD) of mobile atoms is modelled as a multivariate normal distribution that is parametrised from the input simulation data. kinisi uses Markovchain Monte Carlo (ForemanMackey 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., jumpdiffusion coefficients and ionic conductivities.
Original language  English 

Journal  The Journal of Open Source Software 
Publication status  Submitted  17 Aug 2023 
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Dive into the research topics of 'kinisi: Bayesian analysis of mass transport from molecular dynamics simulations'. Together they form a unique fingerprint.Projects
 2 Finished

Next Generation Liion Cathode Materials (CATMAT)
Islam, S. & Morgan, B.
Engineering and Physical Sciences Research Council
1/10/19 → 30/09/23
Project: Research council

Computational Discovery of Conduction Mechanisms in LithiumIon Solid Electrolytes
1/10/19 → 30/09/22
Project: Research council