The main page about
Adaptive Molecular Dynamics Simulation (
AMoDS)
Project Overview
The omnipresent
Sampling Problem is one of the big issues in
Molecular Dynamics Simulation. So far the amount of information, that
needs to be computed is too large, so that one cannot simulate processes
of timescales of general interest. This means, that we cannot
circumvent the
Sampling Problem, the only ways we can deal with
this is to either increase the computational effort or simulate at some degree
of approximation.
A third not often taken option is to restrain the simulation not to the full information
contained in the phase-space, but concentrate the simulation on a
certain kind and leave all other information aside.
In every case a certain amount of information costs a certain amount
of computational ressources, we will follow the third option and try
to intelligently design a set of short simulations, that will give a maximum
of information about a specific, preselected information about the
system. This, of course, has the drawback, that the gained information
might be of lesser use for other problems.
To achieve a guidance of the simulation without interfering or biasing,
we divide the available simulation into chunks of short trajectories
(s. \prettyref{fig:enhancedSampling}). Each of this simulation parameters
are chosen carefully, to maximize the information in the target property.
The Adaptive Cycle
The Adaptive Cycle consists of four steps, that are applied until a certain amount of
convergence of the target property is achieved. Given a first trajectory as input :
- Clustering of the given trajectory into metastable states
- Prediction of Transition Matrices depending on various initial condition and information from previous trajectory
- Computing of Errors in the target property for each Prediction
- Running a simulation with the initial conditons, that reduce the error most
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