Simulations microscopic

P. J. Hoogerbrugge and J. M. V. A. Koelman, Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics, Europhys. Lett. 19, 155 (1992). 

Carmesin, I. and Kremer, K. The bond fluctuation method a new effective algorithm for the dynamics of polymers in all spatial dimensions. Macromolecules, 21,2819-23 (1988). Hoogerbrugge, P. J. and Koelman, J. M. V. A. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhys Lett., 19,155-60 (1992). 

Our discrete-particle approach possesses the important properties of mesoscopic systems. It can model easily the heterogeneous nature of complex fluid suspension in the presence of fluctuations. This allows for simulating processes, which cannot be modeled by computational fluid dynamics codes. We showed that our microscopic blood model can be used for simulating microscopic, multi-component blood flow under extreme conditions in presence of high acceleration [100]. 

A catalyst may play an active role in a different sense. There are interesting temporal oscillations in the rate of the Pt-catalyzed oxidation of CO. Ertl and coworkers have related the effect to back-and-forth transitions between Pt surface structures [220] (note Fig. XVI-8). See also Ref. 221 and citations therein. More recently Ertl and co-workers have produced spiral as well as plane waves of surface reconstruction in this system [222] as well as reconstruction waves on the Pt tip of a field emission microscope as the reaction of H2 with O2 to form water occurred [223]. Theoretical simulations of these types of effects have been reviewed [224]. 

Progress in the theoretical description of reaction rates in solution of course correlates strongly with that in other theoretical disciplines, in particular those which have profited most from the enonnous advances in computing power such as quantum chemistry and equilibrium as well as non-equilibrium statistical mechanics of liquid solutions where Monte Carlo and molecular dynamics simulations in many cases have taken on the traditional role of experunents, as they allow the detailed investigation of the influence of intra- and intemiolecular potential parameters on the microscopic dynamics not accessible to measurements in the laboratory. No attempt, however, will be made here to address these areas in more than a cursory way, and the interested reader is referred to the corresponding chapters of the encyclopedia. 

We carry out computer simulations in the hope of understanding bulk, macroscopic properties in temis of the microscopic details of molecular structure and interactions. This serves as a complement to conventional experiments, enabling us to leam something new something that cannot be found out in other ways. 

Computer simulations act as a bridge between microscopic length and time scales and tlie macroscopic world of the laboratory (see figure B3.3.1. We provide a guess at the interactions between molecules, and obtain exact predictions of bulk properties. The predictions are exact in the sense that they can be made as accurate as we like, subject to the limitations imposed by our computer budget. At the same time, the hidden detail behind bulk measurements can be revealed. Examples are the link between the diffiision coefficient and... 

Figure B3.3.1. Simulations as a bridge between the microscopic and the macroscopic. We mput details of molecular structure and interactions we obtain predictions of phase behaviour, structural and time-dependent properties.

A consideration of the transition probabilities allows us to prove that microscopic reversibility holds, and that canonical ensemble averages are generated. This approach has greatly extended the range of simulations that can be perfonned. An early example was the preferential sampling of molecules near solutes [77], but more recently, as we shall see, polymer simulations have been greatly accelerated by tiiis method. 

Monte Carlo simulations generate a large number of confonnations of tire microscopic model under study that confonn to tire probability distribution dictated by macroscopic constrains imposed on tire systems. For example, a Monte Carlo simulation of a melt at a given temperature T produces an ensemble of confonnations in which confonnation with energy E. occurs witli a probability proportional to exp (- Ej / kT). An advantage of tire Monte Carlo metliod is tliat, by judicious choice of tire elementary moves, one can circumvent tire limitations of molecular dynamics techniques and effect rapid equilibration of multiple chain systems [65]. Flowever, Monte Carlo... 

The avidin-biotin complex, known for its extremely high affinity (Green, 1975), has been studied experimentally more extensively than most other protein-ligand systems. The adhesion forces between avidin and biotin have been measured directly by AFM experiments (Florin et al., 1994 Moy et al., 1994b Moy et al., 1994a). SMD simulations were performed on the entire tetramer of avidin with four biotins bound to investigate the microscopic detail of nnbinding of biotin from avidin (Izrailev et al., 1997). 

Abstract. Molecular dynamics (MD) simulations of proteins provide descriptions of atomic motions, which allow to relate observable properties of proteins to microscopic processes. Unfortunately, such MD simulations require an enormous amount of computer time and, therefore, are limited to time scales of nanoseconds. We describe first a fast multiple time step structure adapted multipole method (FA-MUSAMM) to speed up the evaluation of the computationally most demanding Coulomb interactions in solvated protein models, secondly an application of this method aiming at a microscopic understanding of single molecule atomic force microscopy experiments, and, thirdly, a new method to predict slow conformational motions at microsecond time scales. 

That simulation study [49] aimed at a microscopic interpretation of single molecule atomic force microscope (AFM) experiments [50], in which unbinding forces between individual protein-ligand complexes have been m( asured... 

Fig. 6. Force profile obtained from a one nanosecond simulation of streptavidin-biotin rupture showing a series of subsequent force peaks most of these can be related to the rupture of individual microscopic interactions such as hydrogen bonds (bold dashed lines indicate their time of rupture) or water bridges (thin dashed lines).

The classical microscopic description of molecular processes leads to a mathematical model in terms of Hamiltonian differential equations. In principle, the discretization of such systems permits a simulation of the dynamics. However, as will be worked out below in Section 2, both forward and backward numerical analysis restrict such simulations to only short time spans and to comparatively small discretization steps. Fortunately, most questions of chemical relevance just require the computation of averages of physical observables, of stable conformations or of conformational changes. The computation of averages is usually performed on a statistical physics basis. In the subsequent Section 3 we advocate a new computational approach on the basis of the mathematical theory of dynamical systems we directly solve a... 

T. Simonson. Accurate calculation of the dielectric constant of water from simulations of a microscopic droplet in vacuum. Chem. Phys. Lett, 250 450-454, 1996. 

Many of the mesoscale techniques have grown out of the polymer SCF mean field computation of microphase diagrams. Mesoscale calculations are able to predict microscopic features such as the formation of capsules, rods, droplets, mazes, cells, coils, shells, rod clusters, and droplet clusters. With enough work, an entire phase diagram can be mapped out. In order to predict these features, the simulation must incorporate shape, dynamics, shear, and interactions between beads. 

Polymers are difficult to model due to the large size of microcrystalline domains and the difficulties of simulating nonequilibrium systems. One approach to handling such systems is the use of mesoscale techniques as described in Chapter 35. This has been a successful approach to predicting the formation and structure of microscopic crystalline and amorphous regions. 

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