Liquids, simulation

An important though deman ding book. Topics include statistical mechanics, Monte Carlo sim illation s. et uilibrium and non -ec iiilibrium molecular dynamics, an aly sis of calculation al results, and applications of methods to problems in liquid dynamics. The authors also discuss and compare many algorithms used in force field simulations. Includes a microfiche containing dozens of Fortran-77 subroutines relevant to molecular dynamics and liquid simulations. 

Lsc th e force fields th at have dern on strated accuracy for particu lar molecules or simulations. For example, CiPLS reproduces physical properties in liquid simulations extremely well. MM+ reproduces the structure and thermodynamic properties of small, nonpolar molecules better than AMBER, BIO+, and OPLS. 

OPTS (Optim i/.ed Potentials for Liquid Simulations) is based on a force field developed by the research group of Bill Jorgensen now at Yale University and previously at Purdue University. Like AMBER, the OPLS force field is designed for calculations on proteins an d nucleic acids. It in troduces non bonded in leraclion parameters that have been carefully developed from extensive Monte Carlo liquid sim u lation s of small molecules. These n on-bonded interactions have been added to the bonding interactions of AMBER to produce a new force field that is expected to be better than AMBER at describing simulations w here the solvent isexplic-... 

Optimized potentials for liquid simulation (OPES) was designed for modeling bulk liquids. It has also seen significant use in modeling the molecular dynamics of biomolecules. OPLS uses five valence terms, one of which is an electrostatic term, but no cross terms. 

Choose initial positions for the atoms. For a molecule, this is whatever geometry is available, not necessarily an optimized geometry. For liquid simulations, the molecules are often started out on a lattice. For solvent-solute systems, the solute is often placed in the center of a collection of solvent molecules, with positions obtained from a simulation of the neat solvent. 

Nearly all liquid simulations have been done using molecular mechanics force fields to describe the interactions between molecules. A few rare simulations have been completed with orbital-based methods. It is expected that it will still be a long time before orbital-based simulations represent a majority of the studies done due to the incredibly large amount of computational resources necessary for these methods. 

Calculating nonbonded interactions only to a certain distance imparts an error in the calculation. If the cutoff radius is fairly large, this error will be very minimal due to the small amount of interaction at long distances. This is why many bulk-liquid simulations incorporate 1000 molecules or more. As the cutoff radius is decreased, the associated error increases. In some simulations, a long-range correction is included in order to compensate for this error. 

Setting up liquid simulations is more complex than molecular calculations. This is because the issues mentioned in this chapter must be addressed. At least the first time, researchers should plan on devoting a significant amount of work to a liquid simulation project. 

OPES (optimized potentials for liquid simulation) a molecular mechanics force field... 

Ewald summation has been applied successfully for many years to liquid simulations [35] and is now becoming a standard for macromolecular simulations [36]. For this reason we focus on Ewald summation for the remainder of this section. 

The major difference of the water structure between the liquid/solid and the liquid/liquid interface is due to the roughness of the liquid mercury surface. The features of the water density profiles at the liquid/liquid interface are washed out considerably relative to those at the liquid/solid interface [131,132]. The differences between the liquid/solid and the liquid/liquid interface can be accounted for almost quantitatively by convoluting the water density profile from the Uquid/solid simulation with the width of the surface layer of the mercury density distribution from the liquid/liquid simulation [66]. 

OPLS is designed for calculations on proteins and nucleic acids the non-bonded interactions have been carefully developed from liquid simulations on small molecules. There are many more force fields in the literature, but the ones given above are representative. 

Jorgensen et al. has developed a series of united atom intermolecular potential functions based on multiple Monte Carlo simulations of small molecules [10-23]. Careful optimisation of these functions has been possible by fitting to the thermodynamic properties of the materials studied. Combining these OPLS functions (Optimised Potentials for Liquid Simulation) with the AMBER intramolecular force field provides a powerful united-atom force field [24] which has been used in bulk simulations of liquid crystals [25-27],... 

Gao J (1997) Toward a molecular orbital derived empirical potential for liquid simulations. J Phys Chem B 101(4) 657-663... 

Patel S, Brooks CL (2004) CHARMM fluctuating charge force field for proteins I parameterization and application to bulk organic liquid simulations. J Comput Chem 25(1) 1—15... 

Jorgensen and col. extended their TIPS (Transferable Intermolecular Potentials for Simulations) [120- 122] to several organic liquids. More recently, they developed a new generation of "effective" potentials, which received the denomination of OPLS (Optimized Potentials for Liquid Simulations) [123-127], The standard OPLS philosophy can be summarized in the following three points 1) to keep the form of the potentials simply and easy to evaluate, 2) to include as few new parameters as possible, 3) to produce structural and thermodynamic properties in reasonable accord with experiment. 

In our first implementation [13, 120, 121, 131] of this idea, we took the transition frequency to be a linear function of this electric field. We determined the coefficients of this linear function by fitting to the ab initio frequencies from water clusters (and in this case the clusters were not surrounded by point charges from the other molecules in the simulation). In the liquid simulation we simply calculate this electric field at every time step and then use this linear map (in this case the electric field was the full Ewald field from the simulation) to determine the frequency. In our later implementation [6, 98] we took the... 

K. Coutinho and S. Canuto, DLCE A General Monte Carlo Program for Liquid Simulation, University of Sao Paulo, 2000. 

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