Crystallization molecular simulation

Finally, we were led to the last stage of research where we treated the crystallization from the melt in multiple chain systems [22-24]. In most cases, we considered relatively short chains made of 100 beads they were designed to be mobile and slightly stiff to accelerate crystallization. We could then observe the steady-state growth of chain-folded lamellae, and we discussed the growth rate vs. crystallization temperature. We also examined the molecular trajectories at the growth front. In addition, we also studied the spontaneous formation of fiber structures from an oriented amorphous state [25]. In this chapter of the book, we review our researches, which have been performed over the last seven years. We want to emphasize the potential power of the molecular simulation in the studies of polymer crystallization. 

In the world of molecular simulation, it would be more conventional to consider that the present model is a coarse grained model of real polymers, where the real time-scale is much longer than that of the present MD simulation time-scale. However, we did not intend to make a coarse grained model. The crystallization of polymers was shown to be rather universal. Various kinds of polymers, either fast crystallizing or slow crystallizing, were known to follow the same scheme with respect to the molecular mechanism of crystallization. So we studied this simple model expecting that the present model would also follow the same crystallization scheme and show the general molecular mechanisms of polymer crystallization. 

We will then examine other flexible polymer crystallization instances which may be interpreted, at least qualitatively, in terms of the bundle model. We will concentrate on crystallization occurring through metastable mesophases which develop by quenching polymers like isotactic polypropylene, syndiotactic polypropylene etc. In principle also hexagonal crystallization of highly defective polymers, and order developing in some microphase-separated copolymer systems could be discussed in a similar perspective but these two areas will be treated in future work. A comparison between the bundle approach and pertinent results of selected molecular simulation approaches follows. 

This simulation and other computational studies were carried out with the Polymorph Predictor and Crystal Packer modules in the Cerius2 suite of programs from Molecular Simulations. We thank MSI (Cambridge and San Diego) for their continuing cooperation and assistance. 

Ideally, MD or MC gives a complete description of the equilibrium states of liquids and crystals, and a molecular-level picture of any chemical process occurring within the system, including phase transitions. The limitations are obvious. The calculation is heavy, with some 5,000 molecules at most, and times or time-equivalents of the order of at most milliseconds. Force fields are by necessity restricted to atom-atom empirical ones. One gets at best a blurred and very short glimpse of the simulated process. And yet, appropriately designed molecular simulation is, for example, the only access to molecular aspects of chemical evolution involved in crystal nucleation and growth. 

Molecular aggregation structure analysis and molecular simulation of crystals and liquids... 

An example drawn from Deitrick s work (Fig. 2) shows the chemical potential and the pressure of a Lennard-Jones fluid computed from molecular dynamics. The variance about the computed mean values is indicated in the figure by the small dots in the circles, which serve only to locate the dots. A test of the thermodynamic goodness of the molecular dynamics result is to compute the chemical potential from the simulated pressure by integrating the Gibbs-Duhem equation. The results of the test are also shown in Fig. 2. The point of the example is that accurate and affordable molecular simulations of thermodynamic, dynamic, and transport behavior of dense fluids can now be done. Currently, one can simulate realistic water, electrolytic solutions, and small polyatomic molecular fluids. Even some of the properties of micellar solutions and liquid crystals can be captured by idealized models [4, 5]. 

To understand the conduction of water through AQP channels such as the GlpF channel, the crystal structure of GlpF with only water in the channel at 2.7 A that reveals the mean positions and probabilities of position can be compared with a molecular simulation in which 7-9 water... 

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