# Re:

Date: Thu Mar 15 2018 - 08:24:07 CDT

I think you are looking for the *mean* acceptance probability, that is, the
average probability with which an exchange is accepted. You can compute
this in two different ways which, to my knowledge, are essentially
equivalent for any reasonably large number of REMD cycles:

1) take the expectation of the Metropolis criterion P = min[1,
exp(-Delta_ij)] where Delta_ij contains the observed energies and
temperatures -- this method is cumbersome and requires sifting through a
lot of data

2) just divide the number of success by the number of attempts = (50 / 450)
= 0.11 = 11%

The reasonableness of the observed value depends on what your exchange
scheme is. I assume that your script implements a nearest neighbor
sampling? If the neighbors are chosen stochastically with equal
probabilities, then 11% is very close to optimal. If the neighbors are
chosen in the deterministic "up/down" strategy then something closer to 20%
is preferred.

If you are unhappy with your acceptance probability you have two options:
1) assume that you have bad statistics and keep running until the
performance numbers change or 2) start over and choose more replicas over
the temperature range.

HTH,
BKR

On Thu, Mar 15, 2018 at 7:44 AM, Srijita Paul <srijitap91_at_gmail.com> wrote:

> Hi,
>
> Can anybody explain me the output file obtained from a remd simulation
> .job0.restart900.0.tcl.
>
> array set replica {index.b 4 index 3 temperature.a 283.48
> exchanges_attempted 450 loc.a 4 temperature.b 289.07 temperature 286.26
> exchanges_accepted 50 loc.b 18 index.a 2}
>
> exchanges_attempted 450
> exchanges_accepted 50
>
> Is it a good result for remd? How can I find acceptance probability for my
> system?
>

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