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  Optimal estimates of self-diffusion coefficients from molecular dynamics simulations

Bullerjahn, J. T., von Bülow, S., & Hummer, G. (2020). Optimal estimates of self-diffusion coefficients from molecular dynamics simulations. The Journal of Chemical Physics, 153(2): 024116. doi:10.1063/5.0008312.

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 Creators:
Bullerjahn, Jakob T.1, Author              
von Bülow, Sören1, Author              
Hummer, Gerhard1, 2, Author              
Affiliations:
1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
2Institute of Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany, ou_persistent22              

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 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 regular diffusion caused by systematic errors in a common trajectory "unwrapping" scheme that is implemented in popular simulation and visualization software.

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Language(s): eng - English
 Dates: 2020-03-202020-06-182020-07-092020-07-14
 Publication Status: Published in print
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/5.0008312
 Degree: -

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Title: The Journal of Chemical Physics
  Other : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 153 (2) Sequence Number: 024116 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226