Results of Generic Model Simulations

C. Zannoni [ Datareview in this book 12.2 Results of generic model simulations ]... 

The order parameter is directly available from the calculations and the SCF results are given in Figure 17. The absolute values of the order parameter are a strong function of head-group area. Unlike in most SCF models, we do not use this as an input value it comes out as a result of the calculations. As such, it is somewhat of a function of the parameter choice. The qualitative trends of how the order distributes along the contour of the tails are rather more generic, i.e. independent of the exact values of the interaction parameters. The result in Figure 17 is consistent with the simulation results, as well as with the available experimental data. The order drops off to a low value at the very end of the tails. There is a semi-plateau in the order parameter for positions t = 6 — 14,... 

The computer simulations are likely to be useful in two distinct situations— the first in which numerical data of a specified accuracy are required, possibly for some utilitarian purpose the second, perhaps more fundamental, in providing guidance to the theoretician s intuition, e.g., by comparing numerical results with those from approximate analytical approaches. As a consequence, the physical content of the model will depend upon the purpose of the calculation. Our attention here will be focused largely on the coarse-grained (lattice and off-lattice) models of polymers. Naturally, these models should reflect those generic properties of polymers that are the result of the chainlike structure of macromolecules. 

Fig. 7. Pocket fumigation is a modeling technique based on torsional sampling in the presence of a repulsive density representing a generic ligand, (a) the original X-ray structure (b) the result of Ala conversion the largest pocket density is generated (c) a druggable" pocket conformation obtained by Monte Carlo simulation in the presence of the density.

Generic features of the results for both HHG and ATI spectra obtained in such model simulations have been recently provided (Blase 2001). In particular, the simulations... 

A variety of different models of the interface between water and a solid phase have been used in computer simulations. As far as the solid is concerned, a basic distinction can be made between smooth solid phases without atomic structure on the one hand and corrugated surfaces on the other. The latter surfaces have been modeled as rigid (frozen) or flexible atomic lattices representing the solid phase [47-51] or as a corrugated external potential that describes the effect of the solid phase by a more or less elaborate potential function F(x,y,z) [52-56]. The generic metallic features are modeled by treating the metal phase as a medium of infinite dielectric constant or by using the jellium model (e.g.. Ref. 57-59). In several cases, the results of semi-empirical and ab initio quantum chemical calculations have been parametrized [40, 48, 55]. 

Early atomistic simulations employed pair potentials, usually of the Morse or Lennard-jones type (Figure 11.6). Although such potentials have been and still are a useful model for fundamental studies of generic properties of materials, the agreement between simulation results and experiment can only be quantitative at best. While such potentials can be physically justified for inert elements and perhaps some ionic solids, they do not capture the nature of interatomic bonding even in simple metals, not to mention transition metals or covalent solids. 

For particular cases, it maybe required to add more complex phenomena with additional effects or more evolved descriptions of the same mechanisms. In general, however, reduced models are appropriate and desirable. Historically, this stemmed from the shorter computational effort and time required for the numerical solution of such models. Today this is also an advantage for optimization, control, and real-time simulation applications, and reliable simplified models are still used for almost all purposes due to the lower number of dimensionless parameters requiring estimation and to the success found in the description of experimental results. On the other hand, complex detailed models fulfill the most generic purpose of reactor simulation, which is related to the prediction of the actual behavior from fundamental, independently measured parameters. Therefore, it is important to understand the equivalence and agreement between both detailed and reduced models, so as to take advantage of their predictive power without unnecessary effort. 

The simulation put forward in section 2.5 uses a certain number of parameters. The approach we now advocate is not to set preconceived values for these parameters, but instead to identify them on the basis of systematic experimental characterizations. Thus, if the model is generic, the result of the parameterization will ultimately always be specific to the particular electrolyzer in question. The experimental characterizations upon which this section is based are those presented in section 2.6. 

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