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Simulation, molecular

Although simple arguments based on molecular packing or film curvature can rationalize the structures observed in many experimental systems, one would like to calculate aggregate [Pg.554]

In this thesis, we will use force field based computational methods. This means that we know exactly all interactions between the atoms of our system. Once we know these interactions, we are able to calculate a variety of static and dynamic properties like heats of adsorption, adsorption isotherms, and diffusion coefficients. In general, there are two methods to obtain a molecular force field  [Pg.2]

From quantum mechanical calculations. By solving the Schrodinger equation using various approximations, we can obtain forces between different atoms and molecules. These forces can be fitted into a force field. This usually works very well for intra-molecular bonded interactions like bond-stretching, bond-bending, and torsion interactions, but less well for van der Waals interactions. Note that hydrocarbon-zeolite interactions are dominated by van der Waals interactions (see, for example, ref. [24] and chapter 4). Recently, there have been several quantum-mechanical studies of water and methanol in Sodalite [25,26] using the Car-Parrinello technique [27], [Pg.3]

From experimental data. A force field can be fitted in such a way that experimental data like diffusion coefficients, heats of adsorption, or phase equilibria can be reproduced. This force field can then be used to compute other properties of other molecules. [Pg.3]

Once we have a force field, we can calculate dynamic and static properties of our system. In general, there are two classes of methods  [Pg.3]

Except for a few trivial cases, these equations cem only be solved numerically for a system of more than two particles. A popular cdgorithm to solve these equations of motion is the so called velocity-Verlet cdgorithm [28,29]  [Pg.3]

Because measurement of the phase behaviour of ionic liquid systems is time consuming, it is desirable to develop predictive methods to estimate this. Different approaches were proposed for modeling the phase behaviour of ionic liquid systems (i) molecular simulations, (ii) excess Gibbs-energy methods, (iii) equation of state modeling and (iv) quantum chemical methods. [Pg.379]

Molecular simulations have been used to obtain thermodynamic properties and phase equilibria data of ionic liquid systems (i) Monte Carlo simulation techniques were employed to predict the solubility of gases and water in ionic liquids and (ii) molecular dynamics simulations were used to investigate the solvation dynamics of water and various organics in ionic liquids.  [Pg.379]

The reliability of these molecular simulations depends both on the quality of the force field and on the use of a proper simulation method.  [Pg.379]

The most common approach to develop a classieal foree field for an ionie liquid is to represent the bonding and energetics with simplified analytical potential functions, which are inexpensive to evaluate numerically. Such a force field is typically based on an OPLS-AA/AMBER framework with two types of torsions (proper and improper) and has the following form for the total potential energy [Pg.379]

The first four terms represent the bonded interactions that is, bonds, angles, proper torsions and improper torsions. In eq 11.1, kb is the bond force constant, (/ — /q) is the distance from equilibrium bond length that the atom has moved, kg is the angle force constant, (0 0q) is th angle from equilibrium between three [Pg.379]

The adsorption capacity calculated by the GCMC simulation is the absolute uptake Nab, while the experiment data usually are the excess uptake Nex-In order to conveniently compare the simulation results with the experiment data. Nab is usually converted into Nex by [Pg.125]

The isosteric heat of adsorption qst is an important thermodynamic quantity, because it can reflect the interaction strength of the adsorbates and the materials. It is the released heat for each molecule added to the adsorbed phase, given by  [Pg.125]

The GCMC simulation can be performed by MUSIC code and also the Sorption model of Materials Studio. Of course, the GCMC programs [Pg.125]


There are many large molecules whose mteractions we have little hope of detemiining in detail. In these cases we turn to models based on simple mathematical representations of the interaction potential with empirically detemiined parameters. Even for smaller molecules where a detailed interaction potential has been obtained by an ab initio calculation or by a numerical inversion of experimental data, it is usefid to fit the calculated points to a functional fomi which then serves as a computationally inexpensive interpolation and extrapolation tool for use in fiirtlier work such as molecular simulation studies or predictive scattering computations. There are a very large number of such models in use, and only a small sample is considered here. The most frequently used simple spherical models are described in section Al.5.5.1 and some of the more common elaborate models are discussed in section A 1.5.5.2. section Al.5.5.3 and section Al.5.5.4. [Pg.204]

This chapter concentrates on describing molecular simulation methods which have a counectiou with the statistical mechanical description of condensed matter, and hence relate to theoretical approaches to understanding phenomena such as phase equilibria, rare events, and quantum mechanical effects. [Pg.2239]

Frenkel D and Smit B 1996 Understanding Molecular Simulation From Algorithms to Applications (San Diego Academic)... [Pg.2281]

Kofke D A and Cummings P T 1997 Quantitative comparison and optimization of methods for evaluating the chemical potential by molecular simulation Mol. Phys. 92 973-96... [Pg.2283]

Deitrick G L, Scriven L E and Davis H T 1989 Efficient molecular simulation of chemical potentials J. Chem. Phys. 90 2370-85... [Pg.2284]

Vlugt T J H, Krishna R and Smit B 1999 Molecular simulations of adsorption isotherms for linear and branched alkanes and their mixtures in silicalite J. Phys. Ohem. B 103 1102-18... [Pg.2285]

One of the most comprehensive and up-to-date simnner school proceedings, with contributions from many of the world s leading experts. Almost every aspect of molecular simulation is covered. [Pg.2290]

For larger systems, various approximate schemes have been developed, called mixed methods as they treat parts of the system using different levels of theory. Of interest to us here are quantuin-seiniclassical methods, which use full quantum mechanics to treat the electrons, but use approximations based on trajectories in a classical phase space to describe the nuclear motion. The prefix quantum may be dropped, and we will talk of seiniclassical methods. There are a number of different approaches, but here we shall concentrate on the few that are suitable for direct dynamics molecular simulations. An overview of other methods is given in the introduction of [21]. [Pg.252]

This conference on Algorithms for Molecular Simulation is a good occasion to pause for a moment and consider where we are now and where we go in this field. The book by Allen and Tildesley [2] describes most of the techniques that are still in use today. [Pg.3]

Pree energy via molecular simulation A primer. In Computer Simulations of Biomolecular Systems, Vol 2, W.F. van Gunsteren, P.K. Weiner and A.J. Wilkinson, eds. Escom, Leiden (1993) 267-314. [Pg.28]

Schlitter et al., 1993] Schlitter, J., Engels, M., Kruger, P., Jacoby, E., and Wollmer, A. Targeted molecular dynamics simulation of conformational change - application to the t <- r transition in insulin. Molecular Simulation. 10 (1993) 291-308... [Pg.64]

B. A. Luty, I. G. Tironi, and W. F. van Gunsteren. Lattice-sum methods for calculating electrostatic interactions in molecular simulations. J. Chem. Phys., 103 3014-3021, 1995. [Pg.96]

U. Essmann, L. Perera, M. L. Berkowitz, T. Darden, H. Lee, and L. G. Pedersen. The smooth particle mesh ewald method. J. Chem. Phys., 103 8577, 1995. Brock A. Luty, Ilario G. Tironi, and Wilfried F. van Gunsteren. Lattice-sum methods for calculating electrostatic interactions in molecular simulations. J. Chem. Phys., 103 3014-3021, 1995. [Pg.96]

Jayaram, B., Beveridge, D. L. A simple method to estimate free energy from molecular simulation Renormalization on the unit interval. J. Phys. Chem. 94 (1990) 7288-7293... [Pg.162]

Beutler, T. C., Mark, A. E., van Schaik, R. C., Gerber, P. R., van Gunsteren, W. F. Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chem. Phys. Letters 222 (1994) 529-539... [Pg.162]

Molecular Simulations, Inc. Insightll. Molecular Simulations, Inc., Waltham, MA, 1992. [Pg.196]

D. Frenkel and B. Smit. Understandinq Molecular Simulation. Academic Press, 1996. [Pg.315]

H. Grubmiiller, H. Heller, A. Windemuth, and K. Schulten. Generalized Verlet algorithm for efficient molecular dynamics simulations with long-range interactions. Molecular Simulation, 6 121-142, 1991. [Pg.329]

Grubmiiller, H., Heller, H., Windemuth, A., Schulten, K. Generalized Verlet Algorithm for Efficient Molecular Dynamics Simulations with Long-range Interactions. Molecular Simulation 6 (1991) 121-142... [Pg.348]

In the context of molecular simulation, particularly biomolecular modelling, a critical aspect for numerical simulation is the presence of long-range Coulombic forces which render the force computations much more costly... [Pg.349]

These various techniques were recently applied to molecular simulations [11, 20]. Both of these articles used the rotation matrix formulation, together with either the explicit reduction-based integrator or the SHAKE method to preserve orthogonality directly. In numerical experiments with realistic model problems, both of these symplectic schemes were shown to exhibit vastly superior long term stability and accuracy (measured in terms of energy error) compared to quaternionic schemes. [Pg.352]

As an illustration, we consider a simple example of a top with a fixed point at the center of mass moving in an applied field not dissimilar from those encountered in molecular simulations. Specifically, we used... [Pg.358]

W. F. van Gunsteren, H.J.C. Berend-sen, Groningen Molecular Simulation (CROMOS) Library Manual, Biomos, Nijenborgh 16, Groningen, The Netherlands, 1987. [Pg.357]

CASTEP Molecular Simulations Inc., 9685 Scranton Road, San Diego, California, USA. [Pg.17]

When using a switching function in a molecular simulation with a residue-based cutoff it is important that the function has the same value for all pairs of atoms in the two interacting groups. Otherwise, severe fluctuations in the energy can arise when the separation is within the cutoff region. These two contrasting situations can be formally expressed as follows ... [Pg.347]

Luty B A, M E David, I G Tironi and W F van Gunsteren 1994. A Comparison of Particle-Particle, Particle-Mesh and Ewald Methods for Calculating Electrostatics Interactions in Periodic Molecular Systems. Molecular Simulation 14 11-20. [Pg.365]

Kim K S, D J Tildesley and N Quirke 1994b. Molecular Dynamics of Langmuir-Blodgett Films Bilayers. Molecular Simulation 13 101-114. [Pg.423]

Yun-Yu S, W Lu and W F van Gunsteren 1988. On the Approximation of Solvent Effects on Conformation and Dynamics of Cyclosporin A by Stochastic Dynamics Simulation Teclmiqi Molecular Simulation 1 369-383. [Pg.425]


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