Hi Berk,
Thanks for your reply.
The box is read, but in several parts of the code there are
optimizations
for fixed box dimensions (i.e. no pcoupl, no deform).
This is indeed a bit tricky, but I don't know if we would want to
change
this
for the rerun option.
Maybe the optimizations are not necessary for the rerun option, as
energy evaluations usually involve a limited number of frames. I
think that the box sizes should really be read from the traj. The
present behavior seems dangerous (I almost wasted a week of work
figuring it out, and some users might never double check...)
We do want scaling of velocties, since this influences the kinetic
energy,
also for reruns.
I never understood this one. The kinetic energy at step N should
depend only on the velocities at time step N. If a frame in the
trajectory contains the positions and velocities at time step N as it
should, no rescaling is necessary.
If on the other hand only velocities at step N-1/2 are saved,
velocities at step N+1/2 would have to be calculated in order to get
accurate kinetic energy at step N. In this case, rescaling indeed
needs to take place. But the rescaling will be nonsense if a Nosé-
Hoover thermostat is used, since the thermostat momentum is not read
from the trajectory. If this is the case, there should be a large
warning sign when using -rerun with Nosé-Hoover. A solution would be
to output the kinetic energy at time step N-1/2 during the rerun,
which is in any case more accurate than the one at step N, and is
just as good for statistics.
See a paper coming soon in JCP:
M. M. A. Cuendet and W. F. van Gunsteren, On the calculation of
velocity-dependent properties in molecular dynamics simulations using
the leap-frog integration algorithm, J. Chem. Phys. (in print) (2007).
We could consider using nstenergy=1 for rerun, but I think that
currently
you should already get the energy as often as for the original run,
unless
you use a trajectory without step numbers (e.g. pdb).
This is good, but without nstenergy=1 the averages are wrong in
md.log and at the end of the run.
Bye,
Michel
==========================================================
Michel Cuendet, Ph.D
Molecular Modeling Group
Swiss Institute of Bioinformatics
CH-1015 Lausanne
Switzerland
==========================================================
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