Speaker
Description
The self-diffusion coefficient for an ionic conductor is routinely estimated from molecular dynamics simulations by "fitting a straight line" to observe the mean-squared displacements (MSDs). Typically, this fitting is performed without considering fundamental concepts of displacements, such as their heteroscedasticity and correlation, leading to a sub-optimal estimation method. Meanwhile, the collective-diffusion coefficient is rarely determined from simulation, with a preference for assuming that the ionic motion is completely uncorrelated. However, this is not necessarily the case for many classes of materials.
I will present a Bayesian scheme for estimating the self-diffusion coefficient from a single simulation trajectory with high statistical efficiency and accurately estimating the uncertainty in the predicted value [1]. I will then discuss how this approach may be used to find the collective-diffusion coefficient. Throughout this seminar, I will introduce kinisi, a software package developed to improve the analysis of molecular dynamics simulation [2], and highlight the relevance of this work to inform the analysis of polarised QENS measurements.
You may enjoy this seminar if you are interested in diffusion, applied mathematics, molecular dynamics simulations, or open-source software.
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A. R. McCluskey, S. W. Coles and B. J. Morgan, J. Chem. Theory Comput, 21(1), 79, 2025.
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A. R. McCluskey, A. G. Squires, J. Dunn, S. W. Coles and B. J. Morgan, Journal of Open Source Software, 9, 5984, 2024.
