[gmx-users] Re: gmx 4.6.1, Expanded ensemble: weird balancing factors

Michael Shirts mrshirts at gmail.com
Tue Jul 16 22:49:17 CEST 2013

Hi, all-

This not a problem with W-L, but is instead something that is wrong
with a particular combination of mdp options that are not working for
expanded ensemble simulations.  W-L can equilibrate to incorrect
distributions because it decreases the weights too fast (more on that
later), but that is not the problem here. The option wl-oneovert
allows switching to a slower scaling that is more likely to converge.

The reason that it is not a W-L problem is that after the WL weights
are equilibrated, it is equilibrium sampling in the expanded ensemble.
 If the W-L equilibration was a problem, then the distribution of
states would not be flat.  They are flat, so therefore the expanded
ensemble dynamics are wrong.

I have example expanded ensemble setups.


These mdp files should work. Note that you should be able to swap out
any top, and it will still work.  If you get CORRECT results (and it
should take just a few ns to see) with these tops, then I will go
through and try to figure out which differences are causing the

Thanks for the patience while we test this new functionality.
Frequently when one puts new code in the hands of others it breaks in
ways that were not foreseen!

On Tue, Jul 16, 2013 at 12:53 PM, Dejun Lin <dejun.lin at gmail.com> wrote:
> I have a similar question here. Can anyone tell if the Wang-Landau algorithm
> in lambda space is robust in that it doesn't depend on the choice of the
> convergence factor (weight-equil-wl-delta), the flatness of the histogram
> (wl-ratio) and/or the frequency of trial move (nstexpanded), especially in
> the case of barely overlapping energy distribution corresponding to
> different lambdas? I can imagine in the extreme case, with only 2 lambdas (
> l = 1 or 0), the gap between the 2 energy distributions might be so large
> such that for most of the time, trial moves from the "center" of
> distribution 1 would frequently end up in the "tail" of distribution 0. In
> this case, the Wang-Landau weights would be biased if there's not enough
> time for the system to relax back to equilibrium.
> Thanks,
> Dejun
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