On Thu, Nov 22, 2012 at 10:13 AM, Felipe Pineda, PhD
luis.pinedadecas...@lnu.se wrote:
Would non-deterministic be correct to characterize the nature of MD as
well? There is also deterministic chaos ...
An MD simulation is normally deterministic, inasmuch as the inputs and
algorithm determine
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
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
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
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
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
MD is chaotic regardless of how differences, however small, are created in the
first place. This was just one example.
Stochastic terms in e.g. the v-rescale thermostat will rely on the same
sequence of random numbers in separate simulations if the random number
generator is seeded in the same
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
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
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)
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
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