Metropolis Monte Carlo simulated annealing

Vanderbilt and Louie (Ref. 43) discuss the use of Gaussian kernels determined by Eq. (2.5) in the closely related situation of Metropolis Monte Carlo-simulated annealing. 

Molecular dynamics can be coupled to a heat bath (see below) so that the resulting ensemble asymptotically approaches that generated by the Metropolis Monte Carlo acceptance criterion (Eq. 10). Thus, molecular dynamics and Monte Carlo are in principle equivalent for the purpose of simulated annealing although in practice one implementation may be more efficient than the other. Recent comparative work (Adams, Rice, Brunger, in preparation) has shown the molecular dynamics implementation of crystallographic refinement by simulated annealing to be more efficient than the Monte Carlo one. 

The success and efficiency of simulated annealing depends on the choice of the annealing schedule [58], that is, the sequence of numerical values Ti > T2 > > T for the temperature. Note that multiplication of the temperature T by a factor s is formally equivalent to scaling the target E by 1/s. This applies to both the Monte Carlo as well as the molecular dynamics implementation of simulated annealing. This is immediately obvious upon inspection of the Metropolis Monte Carlo acceptance criterion (Eq. 10). For molecular dynamics this can be seen as follows. Let E be scaled by a factor 1/s while maintaining a constant temperature during the simulation. 

The effective use of temperature as a control factor in the Metropolis Monte Carlo process is the most important factor for the success of simulated annealing method. This has inspired an attempt to exploit temperature in a new way. Von Freyberg [21] has proposed a Simulated Shocking protocol for the efficient search for conformations of polypeptides. In this protocol, the temperature jumps between a very low (T = 5 K) and very high temperature... 

Simulated annealing is a global, multivariate optimization technique based on the Metropolis Monte Carlo search algorithm. The method starts from an initial random state, and walks through the state space associated with the problem of interest by generating a series of small, stochastic steps. An objective function maps each state into a value in EH that measures its fitness. In the problem at hand, a state is a unique -membered subset of compounds from the n-membered set, its fitness is the diversity associated with that set, and the step is a small change in the composition of that set (usually of the order of 1-10% of the points comprising the set). While downhill transitions are always accepted, uphill transitions are accepted with a probability that is inversely proportional to... 

An alternative to molecular dynamics based simulated annealing is provided by Metropolis importance sampling Monte Carlo (Metropolis et al., 1953) which has been widely exploited in the evaluation of configurational integrals (Ciccotti et al., 1987) and in simulations of the physical properties of liquids and solids (Allen and Tildesley, 1987). Here, as outlined in Chapters 1 and 2, a particle or variable is selected at random and displaced both the direction and magnitude of the applied displacement within standard bounds are randomly selected. The energy of this new state, new, is evaluated and the state accepted if it satisfies either of the following criteria ... 

Two types of classical simulations were used in the course of the present work. Monte Carlo and molecular dynamics "annealing" calculations were used to determine the equilibrium structures, while isoenergetic molecular dynamics runs at a wide range of energies were used to determine the melting temperatures. Our Monte Carlo calculations used the Pangali et al. force-biased modification of the basic Metropolis et al. ... 

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