Optimal estimates of diffusion coefficients from molecular dynamics simulations

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 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 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 a companion paper [J. Chem. Phys. XXX, YYYYY (2020)], we demonstrate its ability to recognize deviations from regular diffusion caused by systematic errors in a common trajectory "unwrapping" scheme that is implemented in popular simulation and visualization software.

15 pages, 6 figures. The following article has been accepted for publication at The Journal of Chemical Physics

References in corpus (2)

• Canonical sampling through velocity-rescaling
• Systematic errors in diffusion coefficients from long-time molecular dynamics simulations at constant pressure

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• Bayesian Estimation of the Hydroxyl Radical Diffusion Coefficient at Low Temperature and High Pressure from Atomistic Molecular Dynamics
• H-bonds in Crambin: Coherence in an alpha helix
• Improved mean squared displacement analysis for anomalous single particle trajectories