This work discusses scripts for processing molecular simulations data written using the software package R: A Language and Environment for Statistical Computing. These scripts, named moleculaRnetworks, are intended for the geometric and solvent network analysis of aqueous solutes and can be extended to other H-bonded solvents. New algorithms, several of which are based on graph theory, that interrogate the solvent environment about a solute are presented and described. This includes a novel method for identifying the geometric shape adopted by the solvent in the immediate vicinity of the solute and an exploratory approach for describing H-bonding, both based on the PageRank algorithm of Google search fame. The moleculaRnetworks codes include a preprocessor, which distills simulation trajectories into physicochemical data arrays, and an interactive analysis script that enables statistical, trend, and correlation analysis, and other data mining. The goal of these scripts is to increase access to the wealth of structural and dynamical information that can be obtained from molecular simulations.

A new method for analyzing molecular dynamics simulation data is employed to study the solvent shell structure and exchange processes of mono-, di-, and trivalent metal cations in water. The instantaneous coordination environment is characterized in terms of the coordinating waters' H-bonding network, orientations, mean residence times, and the polyhedral configuration. The graph-theory-based algorithm provides a rapid frame-by-frame identification of polyhedra and reveals fluctuations in the solvation shell shape--previously unexplored dynamic behavior that in many cases can be associated with the exchange reactions of water between the first and second solvation shells. Extended solvation structure is also analyzed graphically, revealing details of the hydrogen bonding network that have practical implications for connecting molecular dynamics data to ab initio cluster calculations. Although the individual analyses of water orientation, residence time, etc., are commonplace in the literature, their combination with graphical algorithms is new and provides added chemical insight.

The wide compositional differences between conventional and alternative fuels have resulted in much research aimed at determining which alternative fuels can be used, and in what proportions, in conventional engines. Atomic-scale modeling is uniquely positioned to lend insight into this question without extensive large-scale tests. The predictive power such modeling affords could narrow the phase space that must be examined experimentally. This study utilizes molecular dynamics (MD) simulations to predict the properties of a set of pure hydrocarbons, as well as binary and multicomponent surrogate fuel mixtures for alternative fuels created from these pure components. The accuracy and transferability of the modified Lennard-Jones adaptive intermolecular reactive empirical bond-order potential (mod-LJ AIREBO) [J. Comput. Chem.200829601611] was assessed by calculating densities, heats of vaporization, and bulk moduli of pure hydrocarbons and the mixtures of these hydrocarbons, i.e., surrogate fuels. Calculated results were compared to experimentally determined values and to values obtained with the nonreactive, all-atom version of the optimized potential for liquid simulations (OPLS-AA) [J. Am. Chem. Soc.19961181122511236]. The mod-LJ AIREBO potential quantitatively predicts the densities of the pure hydrocarbons and binary mixtures of n-dodecane and 2,2,4,4,6,8,8-heptamethylnonane (isocetane). It is interesting to note, that despite doing an excellent job predicting the densities of the pure hydrocarbons, the performance of the mod-LJ AIREBO potential degrades when predicting the densities of the multicomponent surrogates and mixtures of n-hexadecane and isocetane, implying that it is not straightforward to extend potentials fit with pure compounds to mixtures. The OPLS-AA potential also has difficulty quantitatively predicting the densities of mixtures, although a new parameter set for long-chain hydrocarbons (L-OPLS) [J. Chem. Theory Comput.2012814591470] yields some improvement for binary mixtures. Heat of vaporization predictions using both potentials also agree reasonably well with experiment. Bulk moduli predictions using the mod-LJ AIREBO potential are consistently higher than, and do not quantitatively agree with, the experimental values. In contrast, bulk moduli predictions using the OPLS-AA potential are generally in good agreement with experimental values. Despite the success of the OPLS-AA potential predicting the bulk moduli of individual hydrocarbons, it is unable to quantitatively predict the bulk moduli of the multicomponent surrogates. Interestingly, the use of the L-OPLS parameter set improves density predictions but not predicted bulk moduli values.

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