Molecular behavior, computation

Computational chemistry can play a key role in advancing the scientific enterprise. It can provide the data input for many larger, more complex models and provide us with unique insights into molecular behavior so that we can design and construct new molecules for specific tasks. Computational chemistry has become an established tool in the chemist s toolbox and is being used in broad areas of chemistry to replace experimental measurements and to provide us with improved understanding of molecular behavior. Computation will be the major tool that will enable us to cross the many temporal and spatial scales that characterize environmental science. 

Molecular Behavior Computing the Energy of a Model system... 

We have approached these multi-faceted systems by looking in particular at two local molecular properties the electrostatic potential, P(r) and Vs(r). and the local ionization energy, /s(r). In terms of these, we have addressed hydrogen bonding, lone pair-lone pair repulsion, conformer and isomer stability, acidity/basicity and local polarizability. We have sought to show how theoretical and computational analyses can complement experimental studies in characterizing and predicting molecular behavior. ... 

We will delay a more detailed discussion of ensemble thermodynamics until Chapter 10 indeed, in this chapter we will make use of ensembles designed to render the operative equations as transparent as possible without much discussion of extensions to other ensembles. The point to be re-emphasized here is that the vast majority of experimental techniques measure molecular properties as averages - either time averages or ensemble averages or, most typically, both. Thus, we seek computational techniques capable of accurately reproducing these aspects of molecular behavior. In this chapter, we will consider Monte Carlo (MC) and molecular dynamics (MD) techniques for the simulation of real systems. Prior to discussing the details of computational algorithms, however, we need to briefly review some basic concepts from statistical mechanics. 

The International Union of Pure and Applied Chemistry (IUPAC, 10) proposed a definition of computational chemistry about 15 years after the term was already well established in the lexicon of chemists A discipline using mathematical methods for the calculation of molecular properties or for the simulation of molecular behavior. It also includes, e.g., synthesis planning, database searching, combinatorial library manipulation. The open-ended second part of this statement reflects the disparate opinions of the IUPAC committee and others who contributed comments. 

In this chapter, we have reviewed some of our own work on solvation properties in supercritical fluids using molecular dynamics computer simulations. We have presented the main aspects associated with the solvation structures of purine alkaloids in CO2 under different supercritical conditions and in the presence of ethanol as co-solvent, highlighting the phenomena of solvent density augmentation in the immediate neighborhood of the solute and the effects from the strong preferential solvation by the polar co-solvent. We have also presented a summary of our results for the structure and dynamics of supercritical water and ammonia, focusing on the dielectric behavior of supercritical water as functions of density and temperature and the behavior of excess solvated electrons in aqueous and non-aqueous associative environments. 

The previous analysis may be extended to spatially periodic suspensions whose basic unit cell contains not one, but many particles. Such models would parallel those employed in liquid-state theories, which are widely used in computer simulations of molecular behavior (Hansen and McDonald, 1976). This subsection briefly addresses this extension, showing how the trajectories of each of the particles (modulo the unit cell) can be calculated and time-average particle stresses derived subsequently therefrom. This provides a natural entree into recent dynamic simulations of suspensions, which are reviewed later in Section VIII. 

MC and MD are versatile techniques that have been shown to be powerful methods of enhancing our understanding of molecular behavior both of carbon surfaces and of the many other solid adsorbents presently in use. Although this chapter has dealt with the basics of computer simulation, there are many areas where simulators have been active that have not been dealt with in the chapter (e.g., see Chapters 5, 6, 8—10, and 15). 

David A. Dixon is a Battelle fellow in the Fundamental Science Directorate at the Pacific Northwest National Laboratory (PNNL), where he previously served as associate director for theory, modeling, and simulation at the William R. Wiley Environmental Molecular Sciences Laboratory. His main research interest is the use of numerical simulation to solve complex chemical problems with a primary focus on the quantitative prediction of molecular behavior. He uses numerical simulation methods to obtain quantitative results for molecular systems of interest to experimental chemists and engineers with a specific focus on the design of new materials and production processes. Before moving to PNNL, he was research fellow and research leader in computational chemistry at DuPont Central Research and Development (1983-1995) and a member of the Chemistry Department at the University of Minnesota, Minneapolis (1977-1983). He earned his B.S. in chemistry from the California Institute of Technology and his Ph.D. in physical chemistry from Harvard University, where he served as a junior fellow of the Society of Fellows, Harvard University. He is a fellow of the American Association for the Advancement of Science, and a fellow of the American Physical Society. He is a recipient of the 1989 Leo Hendrik Baekeland Award presented by the American Chemical Society, the Federal Laboratory Consortium Technology Transfer Award (2000), and the 2003 American Chemical Society Award for Creative Work in Fluorine Chemistry. 

Perhaps more than any other tool, molecular simulation has played an indispensable role in the development of the insights into molecular behavior we have reviewed in this Chapter. Before simulation was possible, even the existence of a stable hard-sphere crystal was in doubt, despite many years of attention to the question. Much of the progress reviewed here has occurred in the past decade, coinciding with the widespread availability of very powerful, inexpensive computers equally important have been advances in molecular simulation methodology as applied to solid phases. 

It will be important to establish and devise computational approaches in conjunction with experimental approaches - eventually, a hybrid approach will be necessary to explain and predict the behavior of complex biological organization and processes in terms of the molecular constituents. Computational modeling of nanoenabled biological systems will require a different approach, as biological systems are dynamic, constantly changing between different states. Therefore, innovative software and other computational tools must be developed that appropriately simulate such systems. Experiments will need to be devised that will refine these computational models and approaches. 

The principles of molecular mechanics may be used in a molecular simulation calculation, which is a type of computational statistical me-chanics. The goal of molecular simulation is to analyze a theoretical model of molecular behavior in order to determine the macroscopic properties of a substance. In one approach, known as molecular dynamics (MD), Newton s laws of motion for individual particles and a set of potential energy terms describing the forces on the structures are applied to all of the atoms in the calculation. Integration of the resulting differential equations over a short time period leads to new locations and new velocities for the atoms. 

To explain the situation displayed in Fig. 24.8, Kubat has proposed a cooperative theory of stress relaxation [53,54]. He assumed that single units (metal atoms, polymer chain segments) do not relax individually but clusters of such units relax together. Thus, the Kubat theory is quite general an explains the observed behavior of metals and polymers alike. Molecular dynamics computer simulations have confirmed that indeed cluster relaxations prevail over individual relaxation, and this both for metals [55] and for polymers [56,57]. 

The past and current series of molecular dynamics simulations, either those predominantly discussed here or the many others using other potential forms, have provided useful insight into molecular behavior in glass and crystalline systems. However, newer and better techniques will come forward with the advance of faster computers and more accurate interatomic potentials. While ab initio techniques will also advance in kind, the use of the more simplified models that incorporate the most important features of a system of interest will enable reasonably accurate simulations of much larger systems (0(10 -10 )) or longer time frames than currently available. Such large scale calculations will then fit much more closely to the experimental world and provide better links to experimental data and, more importantly, data interpretation. 

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