Off-lattice simulations

A few groups replace the Lennard-Jones interactions by interactions of a different form, mostly ones with a much shorter interaction range [144,146]. Since most of the computation time in an off-lattice simulation is usually spent on the evaluation of interaction energies, such a measure can speed up the algorithm considerably. For example, Viduna et al. use a potential in which the interaction range can be tuned... 

The model has not been studied very intensely so far in particular, none of the features which are characteristic for amphiphilic systems have been recovered yet. However, it is close enough to the successful vector models and simple enough that it might be a promising candidate for off-lattice simulations of idealized amphiphilic systems in the future. 

Thus random interfaces on lattices can be investigated rather efficiently. On the other hand, much analytical work has concentrated on systems described by Hamiltonians of precisely type (21), and off-lattice simulations of models which mimic (21) as closely as possible are clearly of interest. In order to perform such simulations, one first needs a method to generate the surfaces 5, and second a way to discretize the Hamiltonian (21) in a suitable way. 

Shell, M.S., Debenedetti, P.G., Panagiotopoulos, A.Z. Generalization of the Wang-Landau method for off-lattice simulations. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2002, 66, 056703. 

Off-lattice simulation 597 180/160 tracer experiments 262-266, 488-490 Oligomer 24 Operon 35... 

Clark, M. M., Raff, L. M., and Scott, H. L., Hybrid Monte Carlo method for off-lattice simulation of processes involving steps with widely varying rates. Comput Phys 10,584-590 (1996b). 

The results for the C71 chains also show that the performance of off-lattice simulations must be assessed by looking at all possible aspects of the simulation. Often used criteria, such as internal energy variation, torsional angle distribution function, and radial distribution function [38, 39,45] do not by themselves provide reliable performance indicators. 

The simulation of surfaces typically requires defining an appropriate lattice. These can either be a simple lattice model or off-lattice simulations which attempt to treat sites more exphcitly. The simulation proceeds in essentially the same maimer as described with the one exception that we explicitly follow the surface of the lattice. At any given instant in time the entire surface is surveyed in order to construct a detailed fist of all possible surface events that can occur, including adsorption, desorption, surface reaction, and diffusion. Each possible event is assigned a rate (or rate constant) based on the nature of the event and the explicit molecular environment around each species. The rates (rate constants) for each of these possible events are added together to determine the cumulative probability for that particular event. The computer draws a random number which is then used in Eq. (C5) [a modified version of Elq. (C3)] in order to establish the time step of the next event. 

Note, however, two recent review papers, one on off-lattice MC methods for coarse-grained models of polymeric materials [71] and one on MC applied to block copolymer systems [61], in which off-lattice simulations pertaining to self-assembly are discussed. 

This suggests that in ab initio off-lattice simulations of protein folding, which employ residue -based potentials, LJ(6,2) may be more successful than commonly used LJ(12,6) [12]. Finally, we comment that the training of the LJ type potential was numerically more difficult than the training of the square well... 

In off-lattice simulations in the NVT ensemble the (excess) pressure Ap is usually calculated from the Virial theorem ... 

While every off-lattice simulation predicts the general same behavior, the persistence of the layering from the surfaces and the degree of orientation within the layers depends strongly upon the details of the model. It is there-... 

Because we are interested in systems with rather short-range interactions with a well-defined cut-off, it is inefficient to examine all of the pairs of particles ij at each time-step to determine which ones have a nonzero force between them. An alternative is to divide the simulation cell into smaller cells of size r< -t- r, where is a small skin of order 0.3-0.5ct. Now one simply has to check pairs within neighboring cells to determine which pairs ij have a nonzero force. The extra skin allows one to create a list of neighbors, which only has to be updated every 10-20 time steps. Combining the link cell with a Verlet neighbor table is the most efficient method for doing any off-lattice simulation with short-range interactions. To obtain... 

For off-lattice simulations, how one starts the simulation is usually not as much a concern as on a lattice. One simple technique is to use a simple MC procedure to grow RW chains with the appropriate bond length and some restriction to avoid backfolding. This can be done very simply by requiring that r/ i - r)+i > r where Vo is of order 0.3—1.0[Pg.491]

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