On 1/24/13 6:12 AM, dakoenig wrote:
Hi Justin,

thank you so far. Now I got some more specific questions regarding g_msd
I got my trajectory from 0-30ns. So far it should to be "correct".

I was analyzing this trajectory with g_msd and the following command:

g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 15000
-endfit 16000 -o D_coeff
g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 15000
-endfit 17000 -o D_coeff
g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 15000
-endfit 18000 -o D_coeff
and so on

I observed an slight linear decreasing diffusion coefficient. The error bars
instead were increasing (starting at -beginfit 15000 -endfit 23000)
dramatically. Why do I observe such a behavior?

I don't have a direct answer, but the quality of the resulting calculation depends on the quality of the fit. I don't know how parsing out random parts of the MSD curve necessarily helps you. It should be a fairly straight line with default values of -beginfit and -endfit doing a decent job unless the ends are really weird.

Is this the reason:
http://gromacs.5086.n6.nabble.com/MSD-and-self-diffusion-td4411869.html


I don't see how that's related.

In addition to that I analyzed the trajectory every 1 ns:

Careful, you're not analyzing every ns, you're fitting over every ns and doing the calculation over 30 ns of data.

g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 1000 -endfit
2000 -o D_coeff
g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 2000 -endfit
3000 -o D_coeff
g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0 -e 30000 -beginfit 3000 -endfit
4000 -o D_coeff

Here I observed extreme fluctuations starting at 25ns. The errors bars
starting at 27ns were huge. Why do I observe these extreme error bars and
fluctuation for the last 3/5 ns?


You're using less and less data and getting wider error bars, likely.

I checked my whole trajectory (g_msd -f mdrun.xtc -s mdrun.tpr -nice 0 -b 0
-e 30000) as well, but it is a straight line, without any observable curves.


That's the expected outcome. If you zoom into the very beginning of the data (first few hundred ps or less), there is likely a small curvature that then leads into a long period of linear data.

-Justin

--
========================================

Justin A. Lemkul, Ph.D.
Research Scientist
Department of Biochemistry
Virginia Tech
Blacksburg, VA
jalemkul[at]vt.edu | (540) 231-9080
http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin

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