Not to forget about the additional stochastic term in the V-rescale
thermostat, when it's used. Since the equations are evidently
deterministic, is the chaotic nature of MD just a numerical effect?
The practical point: if the velocities are reset upon a restart from an
equilibrated frame in order to generate multiple, independent
trajectories for statistical purposes, the equilibration will be
probably lost and a new equilibration phase will be needed. Is this correct?
Best,
Felipe
On 11/22/2012 11:12 AM, Erik Marklund wrote:
It will depend on the integration algorithms, parallelization, etc. The
equations are deterministic, but numerical differences may arise e.g. from
different ordering of floating point numbers being added together in different
simulations. The chaotic nature of MD would then have the simulations diverge
over time, but the question is how long it takes for such differences to really
manifest.
Best,
Erik
22 nov 2012 kl. 10.13 skrev Felipe Pineda, PhD:
Would "non-deterministic" be correct to characterize the nature of MD as well?
There is also deterministic chaos ... And what about the outcome of starting several
trajectories from the same equilibrated frame as continuation runs, i.e., using its
velocities? Could they be considered independent and used to extract that valuable
statistics mentioned in a previous posting?
Felipe
On 11/22/2012 10:04 AM, Erik Marklund wrote:
Stochastic and chaotic are not identical. Chaotic means that differences in the
initial state will grow exponentially over time.
Erik
22 nov 2012 kl. 09.52 skrev Felipe Pineda, PhD:
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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