Evolutionary molecular simulation

There is little hope of studying theoretically the complex intermolecular situation sketched in Fig. 13.11 with even modest accuracy, unless evolutionary molecular simulation is employed. Recourse is thus made to the classical arsenal of such simulations, broadly subdivided into Monte Carlo (MC) and Molecular Dynamics (MD). The conceptual foundations of these two methods, and their computational feasibility, are now reviewed again from the perspective of their use for nucleation and growth studies. 

time is a clearly singled out variable in a deterministic simulation based on a postulated force field and on the classical equations of motion. For the simulation of an evolving crystal aggregate, MD has the obvious advantage that the kinetics of the process is transparent, as accretion rates can be immediately described as a function of computational time, although the rate of any molecular process is obviously dependent on the postulated force model. In contrast, there is no apparent time variable in an MC simulation, because evolution steps are random and may randomly affect molecular evolutions which in reality happen on different timescales. If, as is often the case, time in MC is taken as proportional to the number of moves, one is implicitly assuming that all molecular moves occur on the same timescale, perhaps not a very severe approximation in studies of molecular aggregates bound by nearly isotropic van der Waals forces. In a variant of the MC formulation, called kinetic Monte Carlo (KMC) 

MC in which moves are only as described in equation 9.10. In MD, umbrella sampling can be used to drive the system to the desired path through configurational space. 

Towler, C. Tiddy, G. J. The crystallization of glycine polymorphs from emulsions, microemulsions and lamellar phases, Cryst. Growth Des. 2002, 2, 523-527 (optical microscopy and SEM). 

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Evolutionary simulation, that is, molecular simulation that explores a large part of configurational space, like MC or MD, has an incredibly vast range of chemical applications the optimization of conformation in single macromolecules such as... 

Evolutionary simulation is indeed the ideal link between molecular physics and a molecular level understanding of the thermodynamics of condensed phases, and as such is the ideal medium for didactic purposes in chemical thermodynamics. Unfortunately, this approach has not yet penetrated into even the most successful physical chemistry textbooks (e.g. Atkins, P. W., The Elements of Physical Chemistry, 2001, 3rd edn Oxford) that insist on the ideal gas approach to chemical thermodynamics and hardly, if at all, mention molecular simulation. 

The model of evolutionary dynamics has been applied to interpret the experimental data on molecular evolution and it was implemented for computer simulations (Huynen et al., 1996 Fontana and Schuster, 1998). The computer simulations allows one to follow the optimization process in full detail at the molecular level. 

Adell, J. C., and Dopazo, J. (1994) Monte Carlo Simulation in Phylogenies An Application to Test the Constancy of Evolutionary Rates, Journal of Molecular Evolution, 38,305-309. 

We have pursued a plug-and-play strategy with two different energy functions, a molecular mechanics AMBER force field [131,132] and a simplified energy function, along with two different sampling techniques, evolutionary programming [91] and Monte Carlo simulations [118,119,127,128]. 

Dynamic, or evolutionary simulation random or Newtonian paths are followed to sample a significant portion of phase space, on which averages can Ije taken (as in ordinary molecular dynamics or Monte Carlo) or to force somehow the access to entropy (free energy simulations). Temperature and pressure can be varied. 

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