Re: [gmx-users] how to repeat simulation correctly?

2012-11-23 Thread Mark Abraham
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 the output, even if the model physics being simulated
is not determinstic (e.g. stochastic elements to the integration). One
needs to be clear about which aspect is of interest. The simulation is
often not reproducible, either, because of run-time effects like dynamic
load balancing or differences in hardware/compilers/libraries.


 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?


Of course, sampling a coloured ball from a bag of balls, putting it back in
without letting go, and taking it back out doesn't create a new sample from
the bag. If all the balls were slowly changing colour and you were trying
to sample the distribution of colours, then that in-and-out process might
be a way to create a new sample, but it depends on the timescales
involved...

So yes, you could start from the same set of positions and velocities and
rely on run-time irreproducibility to introduce differences, and chaos to
amplify those over simulation time, in order to reach points from which you
could make statistically independent simulations. Empirically, you'll need
less simulation time to reach that point if you take active steps to make a
significant difference, like changing the velocity of every atom. You need
to re-equilibrate each time you perturb, but that's generally cheaper than
the alternatives.

Mark




 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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 Erik Marklund, PhD
 Dept. of Cell and Molecular Biology, Uppsala University.
 Husargatan 3, Box 596,75124 Uppsala, Sweden
 phone:+46 18 471 6688fax: +46 18 511 755
 er...@xray.bmc.uu.se
 

Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread 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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Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread Erik Marklund
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
 --
 
 -- 
 gmx-users mailing listgmx-users@gromacs.org
 http://lists.gromacs.org/mailman/listinfo/gmx-users
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---
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Dept. of Cell and Molecular Biology, Uppsala University.
Husargatan 3, Box 596,75124 Uppsala, Sweden
phone:+46 18 471 6688fax: +46 18 511 755
er...@xray.bmc.uu.se
http://www2.icm.uu.se/molbio/elflab/index.html

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Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread 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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Dept. of Cell and Molecular Biology, Uppsala University.
Husargatan 3, Box 596,75124 Uppsala, Sweden
phone:+46 18 471 6688fax: +46 18 511 755
er...@xray.bmc.uu.se
http://www2.icm.uu.se/molbio/elflab/index.html



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Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread Erik Marklund
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
 --
 -- 
 gmx-users mailing listgmx-users@gromacs.org
 http://lists.gromacs.org/mailman/listinfo/gmx-users
 * Please search the archive at 
 http://www.gromacs.org/Support/Mailing_Lists/Search before posting!
 * Please don't post (un)subscribe requests to the list. Use the www 
 interface or send it to gmx-users-requ...@gromacs.org.
 * Can't post? Read http://www.gromacs.org/Support/Mailing_Lists
 ---
 Erik Marklund, PhD
 Dept. of Cell and Molecular Biology, Uppsala University.
 Husargatan 3, Box 596,75124 Uppsala, Sweden
 phone:+46 18 471 6688fax: +46 18 511 755
 er...@xray.bmc.uu.se
 http://www2.icm.uu.se/molbio/elflab/index.html
 
 
 -- 
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---
Erik Marklund, PhD
Dept. of Cell and Molecular Biology, Uppsala University.
Husargatan 3, Box 596,75124 Uppsala, Sweden
phone:+46 18 471 6688fax: +46 18 511 755
er...@xray.bmc.uu.se
http://www2.icm.uu.se/molbio/elflab/index.html

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Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread Felipe Pineda, PhD
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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Re: [gmx-users] how to repeat simulation correctly?

2012-11-22 Thread Erik Marklund
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 way. But sure, choosing different seeds would 
be one way of having diverging simulations.

I think your point is correct in principle, e.g. when pressure is concerned. 
That equilibration time must however also be put in relation to how long it 
takes for two simulations to become uncorrelated.

Best,

Erik
22 nov 2012 kl. 11.30 skrev Felipe Pineda, PhD:

 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
 --
 
 -- 
 gmx-users mailing listgmx-users@gromacs.org
 http://lists.gromacs.org/mailman/listinfo/gmx-users
 * Please search the archive at 
 http://www.gromacs.org/Support/Mailing_Lists/Search before posting!
 * Please don't post (un)subscribe requests to the list. Use the www interface 
 or send it to gmx-users-requ...@gromacs.org.
 * Can't post? Read http://www.gromacs.org/Support/Mailing_Lists

---
Erik Marklund, PhD
Dept. of Cell and Molecular Biology, Uppsala University.
Husargatan 3, Box 596,75124 Uppsala, Sweden
phone:+46 18 471 6688  

Re: [gmx-users] how to repeat simulation correctly?

2012-11-21 Thread Mark Abraham
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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Re: [gmx-users] how to repeat simulation correctly?

2012-11-21 Thread Felipe Pineda, PhD
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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--
+---+
| Luis Felipe Pineda De Castro, PhD |
| Computational Chemist - Postdoc   |
| Computational Chemistry and   |
| Biochemistry Laboratory   |
| School of Natural Sciences|
| Linnaeus University   |
| SE-391 82 Kalmar  |
| Norrgård, room 311|
| Sweden - Sverige  |
| Phone:  ++46-480-44 6329  |
| Mobile: ++46-76-8420572   |
| E-Mail: luis.pinedadecas...@lnu.se|
| Web:lnu.se|
+---+

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Re: [gmx-users] how to repeat simulation correctly?

2012-11-21 Thread Mark Abraham
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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 http://lists.gromacs.org/mailman/listinfo/gmx-usershttp://lists.gromacs.org/**mailman/listinfo/gmx-users
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 --
 +-**--+
 | Luis Felipe Pineda De Castro, PhD |
 | Computational Chemist - Postdoc   |
 | Computational Chemistry and   |
 | Biochemistry Laboratory   |
 | School of Natural Sciences|
 | Linnaeus University   |
 | SE-391 82 Kalmar  |
 | Norrgård, room 311|
 | Sweden - Sverige  |
 | Phone:  ++46-480-44 6329  |
 | Mobile: ++46-76-8420572   |
 | E-Mail: luis.pinedadecas...@lnu.se|
 | Web:lnu.se|
 +-**--+

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[gmx-users] how to repeat simulation correctly?

2012-11-20 Thread Albert

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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