Overview of the Results of the Enhanced Sampling Technique so far
We use one large simulation of the MR121-GSGSW (1microsecond) as a system of reference. This has been clustered into 51 resp. 34 states. Each of the smaller 51 states have been used as a starting point for a set of 100 trejectories, so 5100 in total.
In this adaptive run the short trajectories are taken uniquely out of the set of available trajectories generated in advance, so that we do not need to worry about simulations and clustering during the adaptive cycle. After everything is prepared our data consist of a set of discrete simulations with two transitions each.
The problem of this approach is, that we are limited in the number of runs we can start with a certain initial state. Once the number of trajectories is reached we have to stop the simulation before the complete run is finished.
This is right now a problem since we precalculated the trajectories for the 51state model, but used for the adaptive runs the smaller 34state. This imbalences the number of trajectories for each starting state, especially state no.1 has only one distinct trajectory. Once this state is found a crash of the simulation is imminent.
we should mention, that the number of states in the system changes with time. So we start with one initial configuration and explore from there. Since we use a predefined system with a fixed number of states more states than that cannot be found.