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Thanks so much Gaurav<div><br></div><div><br></div><div>Your explication was so clear, and answer all my questions.</div><div><br></div><div><br></div><div>Regards</div><div><br>Ricardo Cuya<br><br><br>> <br>> Message: 1<br>> Date: Mon, 12 Jul 2010 18:56:02 -0400<br>> From: Gaurav Goel <gauravgoeluta@gmail.com><br>> Subject: Re: [gmx-users] g_msd<br>> To: Discussion list for GROMACS users <gmx-users@gromacs.org><br>> Message-ID:<br>> <AANLkTin8BKOORXkdylk1iwlvnnGq0VK8-LXsx_81Wsb2@mail.gmail.com><br>> Content-Type: text/plain; charset=ISO-8859-1<br>> <br>> On Mon, Jul 12, 2010 at 5:22 PM, Ricardo Cuya Guizado<br>> <rcuyag@hotmail.com> wrote:<br>> > Dear gromacs users<br>> > I make a MD of 20 ns of a solute in water<br>> > With the g_msd program the msd vs the time was obtained<br>> > In the plot, I observed a linear behaviour of the MSD from 0 to 15 ns and a<br>> > plateau with no linear tendence at the last 5 ns arpoximately.<br>> > In order to know if the observed plateau was due to the data or is due to<br>> > the way as the algorithm process the data, I divided the MD in two<br>> > trajectories and obtained the msd for each one.<br>> > From 0-10ns, the plot observed shows a linear tendence en the begining and a<br>> > plateau with no linear tendence from 9 to 10 ns.<br>> > From 10-20 ns the plot observed was linear from 10 to 18 ns and not linear<br>> > at the last, the same plateau was observed.<br>> > Comparing the plots there are not equivalent,.<br>> > Why g_msd produces a non linear plot at the last of the calculation and the<br>> > plateau is ever produces.<br>> > Somebody will explain the way as the g_msd algorithm work? and why the plot<br>> > are no equivalent or why there must be equivalent?<br>> <br>> I will explain how the g_msd algorithm works and hopefully that will<br>> answer all your questions above. What you see in the output file is<br>> average-MSD versus time. This average is done over all the particles<br>> in the group you selected and over multiple time origins (this last<br>> option can be selected with the -trestart parameter). Also, time in<br>> column 1 is time difference from the start of your trajectory to<br>> current time.<br>> <br>> E.g., let's say you collected a trajectory over 5 time units and<br>> choose -trestart=1 time unit and -dt=1 time unit.<br>> <br>> dt=1 means you'll have 6 configurations for your analysis (including<br>> the configuration at t=0).<br>> <br>> trestart=1 means you'll have 5 distinct trajectories for your analysis:<br>> Trajectory 1: 0-5<br>> T2: 1-5<br>> T3: 2-5<br>> T4: 3-5<br>> T5: 4-5<br>> <br>> Now you can notice that all 5 trajectories contribute to the average<br>> MSD after 1 time unit (T1-T5), 4 trajectories contribute to the<br>> average MSD after 2 time units (T1-T4), 3 trajectories to the average<br>> MSD after 3 time units (T1-T3), ...., and only one trajectory to the<br>> MSD after 5 time units (T1). Of course, this assumes that trestart is<br>> large enough that all all these trajectories are uncorrelated.<br>> <br>> So, it's clear that longer the time interval at which you want to<br>> evaluate the MSD lesser the number of trajectories used to evaluate<br>> it...and hence, higher error in MSD values at longer times. That might<br>> explain deviation from linear behaviour at long times.<br>> <br>> However, you must be careful in interpreting the MSD data and I<br>> recommend reading some literature on the subject. A plateau in MSD<br>> versus time data might also signify what is called cage motion, in<br>> which a particle or atom is trapped by the surrounding particles and<br>> is not able to move out of that hole on the simulation time scale. If<br>> you want you can send me your MSD versus time data along with some<br>> information on your system (such as potentials, density, temperature<br>> etc.) and I can let you know my comments.<br>> <br>> Few words of caution:<br>> Make sure that the center of mass of your particle (or atom or<br>> molecule) is diffusing several particle diameters. Also, make sure<br>> that you're calculating the self-diffusion coefficient by fitting a<br>> straight line to the linear region of MSD versus time data. You can<br>> either modify the -beginfit and -endfit options... or calculate the<br>> slope of the MSD versus time data using some other software (e.g.,<br>> gnuplot, excel, etc.). If you're doing the latter you'll need to take<br>> a look at the code in gmx_msd.c to know how the diffusion coefficent<br>> is calculated from the slope of MSD versus time data (tog et correct<br>> units, use proper scaling factors, etc.).<br>> <br>> I hope that helped.<br>> <br>> -Gaurav<br>> ><br>> ><br>> ><br>> ><br>> > Regards<br>> > Ricardo Cuya<br>> ><br><br></div> <br /><hr />Jeux Messenger : mettez vos amis au défi! <a href='http://go.microsoft.com/?linkid=9734391' target='_new'>Jeux Messenger!</a></body>
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