Won't this same stochastic nature of MD provide for different,
independent trajectories even if restarted from a previous, equilibrated
frame even without resetting velocities, i.e., as a continuation run
using the velocities recorded in the gro file of the selected snapshot?
Felipe
On 11/22/2012 12:55 AM, Mark Abraham wrote:
Generating velocities from a new random seed is normally regarded as good
enough. By the time you equilibrate, the chaotic nature of MD starts to
work for you.
Mark
On Nov 21, 2012 1:04 PM, "Felipe Pineda, PhD" <luis.pinedadecas...@lnu.se>
wrote:
So how would you repeat the (let be it converged) simulation from
different starting conditions in order to add that valuable statistics you
mention?
I think this was Albert's question
Felipe
On 11/21/2012 12:41 PM, Mark Abraham wrote:
If a simulation ensemble doesn't converge reliably over a given time
scale,
then it's not converged over that time scale. Repeating it from different
starting conditions still adds valuable statistics, but can't be a
replicate. Independent replicated observations of the same phenomenon
allow
you to assess how likely it is that your set of observations reflect the
underlying phenomenon. The problem in sampling-dependent MD is usually in
making an observation (equating a converged simulation with an
observation).
Mark
On Wed, Nov 21, 2012 at 8:12 AM, Albert <mailmd2...@gmail.com> wrote:
hello:
I am quite confused on how to repeat our MD in Gromacs. If we started
from the same equilibrated .gro file with "gen_vel = no" in
md.mdp,
we may get "exactly" the same results which cannot be treated as
reasonable
repeated running. However, if we use "gen_vel=yes" for each round of
running, sometimes our simulation may not converged at our simulated time
scale and we may get two results with large differences.
So I am just wondering how to perform repeated MD in Gromacs in a
correct way so that our results can be acceptably repeated?
thank you very much.
Albert
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