Author: Leland Barnard Acknowledgments to: Amy Bengtson, Saumitra Saha
Computes mean squared displacements and tracer diffusion coefficients from particle position data as a function of time.
Version 1.13 - published on 28 Mar 2014
The source code must be downloaded from github. It does not exist in the pypi package. See Programming for MAST.
This tool takes as input particle position data from methods such as molecular dynamics or kinetic Monte Carlo and computes the mean squared displacement for all particles as a function of time. For a system with multiple types of particles, the mean squared displacement is computed for each particle type. The tracer diffusion coefficient is then calculated from the slope of the mean squared displacement vs time curve.
The tool is based on The Working Man’s Guide to Obtaining Self Diffusion Coefficients from Molecular Dynamics Simulations by Professor David Keffer from UT Knoxville.
This tool reads in atomic position data in the VASP XDATCAR format. This file format begins with the following set of lines:
Name
C1 C2 C3 ...
N1 N2 N3 ...
Direct
The error bars on the mean squared displacements represent a single standard deviation in the measurements of the squared displacements over all time origins.
The error in the diffusion coefficient represents the standard error in the slope of the weighted least squares fit to the mean squared displacement, using the variance in the squared displacements as the error weight.
“The Working Man’s Guide to Obtaining Self Diffusion Coefficients from Molecular Dynamics Simulations” by Professor David Keffer from UT Knoxville, which may be found here: http://www.cs.unc.edu/Research/nbody/pubs/external/Keffer/selfD.pdf
Researchers should cite this work as follows:
Leland Barnard (2014), "Particle Trajectory Diffusion Analysis," https://materialshub.org/resources/diffanalyzer.
@misc { 31,
title = {Particle Trajectory Diffusion Analysis},
month = {Feb},
url = {https://materialshub.org/resources/31},
year = {2014},
author = {Barnard , Leland}
}