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 piecewise 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 KolmogorovSmirnov test, we check for possible anomalous diffusion. We provide an easytouse Python data analysis script for the estimation of diffusion coefficients. As an illustration, we apply the formalism to molecular dynamics simulation data of pure TIP4PD 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.
 Publication:

arXiv eprints
 Pub Date:
 March 2020
 arXiv:
 arXiv:2003.09193
 Bibcode:
 2020arXiv200309193T
 Keywords:

 Physics  Computational Physics;
 Condensed Matter  Statistical Mechanics;
 Physics  Biological Physics
 EPrint:
 15 pages, 6 figures. The following article has been accepted for publication at The Journal of Chemical Physics