90 structure Atomistic ideas

The earliest fully atomistic molecular dynamic (MD) studies of a simplified Nation model using polyelectrolyte analogs showed the formation of a percolating structure of water-filled channels, which is consistent with the basic ideas of the cluster-network model of Hsu and Gierke. The first MD... 

The idea of building models to represent molecular structure is a fundamental tool that is used to understand chemistry and the structures that atoms adopt as they are found in molecules. In the not too distant past, molecular modeling was accomplished using plastic balls and straws. Watson and Crick discovered the structure of DNA using this type of model ° in 1953. These plastic models were truly atomistic in nature in that they treat each atom as a discrete entity, with complex arrangement of electrons treated explicitly. 

The typical approach to developing analytic potential energy functions is to assume a mathematical expression containing a set of parameters that are subsequently fit to a database of physical properties. An effective potential function requires a mathematical expression that both accurately reproduces this database and is transferable to structures and dynamics beyond those to which it is fit. The latter property is especially critical if an atomistic simulation is to have useful predictive capabilities. Whereas an extensive and well-chosen database from which parameters are determined is important, transferability ultimately depends on the chosen mathematical expression. The definitive expression, however, has yet to be developed. Indeed, many different forms are used, ranging from those derived from quantum mechanical bonding ideas to others based on ad hoc assumptions. 

A number of direct ways for linking atomistic and meso-scale melt simulations have been proposed more recently. The idea behind these direct methods is to reproduce structure or thermodynamics of the atomistic simulation on the meso-scale self-consistently. As this approach is an optimization problem, mathematical optimization techniques are applicable. One of the most robust (but not very efficient) multidimensional optimizers is the simplex optimizer, which has the advantage of not needing derivatives, which are difficult to obtain in the simulation. The simplex method was first applied to optimizing atomistic simulation models to experimental data. We can formally write any observable, like, for example, the density p, as a function of the parameters of the simulation model Bj. In Eq. [2], the density is a function of the Lennard-Jones parameters. 

---