Optimal estimates of self-diffusion coefficients from molecular dynamics simulations

The Journal of Chemical Physics
Jakob Tómas BullerjahnGerhard Hummer

Abstract

Translational diffusion coefficients are routinely estimated from molecular dynamics simulations. Linear fits to mean squared displacement (MSD) curves have become the de facto standard, from simple liquids to complex biomacromolecules. Nonlinearities in MSD curves at short times are handled with a wide variety of ad hoc practices, such as partial and piece-wise fitting of the data. Here, we present a rigorous framework to obtain reliable estimates of the self-diffusion coefficient and its statistical uncertainty. We also assess in a quantitative manner if the observed dynamics is, indeed, diffusive. By accounting for correlations between MSD values at different times, we reduce the statistical uncertainty of the estimator and, thereby, increase its efficiency. With a Kolmogorov-Smirnov test, we check for possible anomalous diffusion. We provide an easy-to-use Python data analysis script for the estimation of self-diffusion coefficients. As an illustration, we apply the formalism to molecular dynamics simulation data of pure TIP4P-D water and a single ubiquitin protein. In another paper [S. von Bülow, J. T. Bullerjahn, and G. Hummer, J. Chem. Phys. 153, 021101 (2020)], we demonstrate its ability to recognize deviations from reg...Continue Reading

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Citations

Jul 17, 2020·The Journal of Chemical Physics·Sören von BülowGerhard Hummer
Mar 18, 2021·Journal of Chemical Theory and Computation·François SicardEdina Rosta
May 14, 2021·Protein Science : a Publication of the Protein Society·Matteo PaloniAlessandro Barducci
Jul 10, 2021·The Journal of Chemical Physics·Jakob Tómas Bullerjahn, Gerhard Hummer
Dec 12, 2020·The Journal of Physical Chemistry. B·Wenwei ZhengJeetain Mittal
Oct 17, 2020·The Journal of Physical Chemistry. B·Piotr Kubisiak, Andrzej Eilmes

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