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Molecular dynamics, simulations

MD simulation is a useful tool for the study of small-scale fluid flows, where numerical integration of Newton s law of motion for particles (atoms or molecules) is carried out using [Pg.63]

Researeh and development of the MD simulation method is still progressing. Currently, MD simulations are computationally restricted to very tiny time scales (about tens of nanoseeond) and very large shear rates ( 10 s ). The eost of eomputer time inereases dramatically with increasing number of atoms in the model of moleeular ehains. The growth of computational power and the development of parallel algorithms will help researchers to simulate erystallization for large systems. [Pg.64]

We will briefly discuss the molecular dynamics results obtained for two systems—protein-like and random-block copolymer melts— described by a Yukawa-type potential with (i) attractive A-A interactions (saa 0, bb = sab = 0) and with (ii) short-range repulsive interactions between unlike units (sab 0, aa = bb = 0). The mixtures contain a large number of different components, i.e., different chemical sequences. Each system is in a randomly mixing state at the athermal condition (eap = 0). As the attractive (repulsive) interactions increase, i.e., the temperature decreases, the systems relax to new equilibrium morphologies. [Pg.64]

To visualize the three-dimensional structures of these microphases more clearly, the density of A-segments was measured on a three-dimensional grid. In the A-rich domains the density of A-segments is high ( 1), and in the B-rich domains this density is low ( 0). The dividing surface between the A-rich domains and the B-rich domains is now represented by the isosurface where the density is midway between these values, i.e., 0.5. [Pg.65]

What is most important for our discussion is the fact that the spatial scale r of the segregated structure for protein-like copolymers is appreciably larger than that for random-block copolymers with the same composition and the same average block length. Also, MIST in the protein-like copolymer system occurs at a temperature higher than that of the random-block system, which is in agreement with the prediction of the polymer RISM theory [153]. [Pg.66]

Obviously, these features arise from the difference between the chemical sequences of the two copolymers. [Pg.67]

We may conclude from Section 4.3.1 that treatment of a chemical system using classical mechanics, based on potential calculated by quantum mechanics, should give quite accurate nuclear dynamics, except possibly for protons. The forces are calculated from the PES and included in Newton s equations, which are solved in an iterative way. This model is referred to as molecular dynamics simulation. [Pg.120]

The potential function of a particle corresponds to E IQ) in Equation 4.7, which we write as V(R). It consists of binding forces and forces from nonbonded nuclei groups and other nuclei. What is included in the molecular dynamics simulation depends on how detailed we want to be. Usually, it is sufficient to include CH bonds as a united atom and neglect the motion of the hydrogen atoms. In proteins, we may choose to ignore the bond distances and include only the torsion angles [Pg.120]

Potential functions may be calculated or approximated on the basis of experimental data or taken from calculated results. The bonding potential may be approximated using the harmonic approximation of Equation 4.26  [Pg.121]

Anharmonicities may be ignored at normal temperature. R is the equilibrium distance between the atoms i and j. Equation 4.44 is part of the potential sum for the bonded atoms. [Pg.121]

The potential from bond angles and torsion angles is written in an analogous way  [Pg.121]

Where r is the distance between the two interaction sites i and j. e and o are the LJ interaction parameters for the ij pair. All the parameters involved in the L J potential functions are listed in Table 8.5. [Pg.160]

The cross-parameters for the pair ij are calculated by the Lorentz-Bertherot rale. The cut-off distance for the intermolecular interaction was set at 3.5 [Pg.160]

The simulation cell is divided into subcells. The density of the component k (either methane or ethane) in the Lth subcell is calculated by [Pg.160]

Where superscript L) denotes subcell number L, is the number of the molecules of the component k in the Lth subcell, is the volume of a subcell and is the distance in the x-direction of a subcell. The permeation flux (L ) of the component k in the Lth subcell is calculated by [Pg.160]

DP (A) has two different pore mouths one a large (pore a) and the other a small mouth (pore b). ZP (B) has zigzag shaped pores whose sizes (diameters) are all the same at the pore entry. SP (C) has straight pores which can be called slit-shaped pores. The minimum pore width (W ) is set equal to 0.5 nm for all three types of pores. The membrane thickness, given in Fig. 8.12 asXZ was set equal to 6.3,4.6 and 5.2 nm for DP, ZP and SP membranes. These values are based on the arrangement of micro-graphite crystallites (shown as a nodule in the figiue) whose unit size was fixed at 2.7 x 3.4 x 1.3 nm for the DP and ZP membranes. [Pg.160]

The pair potential employed was modified fi om that of the Born-Mayer-Huggins type used by Woodcock et al. without the dispersion terms [16], [Pg.152]

The result is a trajectory that specifies how the positions and velocities of the particles vary with time. In general the time increment is set to a few femtoseconds, in some cases even less. [Pg.191]

The potential energy of a single atom has to account for all the interaction energies with the atoms of the molecule, with those of the zeolite wall, and also for the internal bond bending, vibration, etc. Dual interactions are calculated [Pg.191]

The self diffusivity is obtained from the trajectory by means of the Einstein relation (3.5.4-1). [Pg.192]

Molecular Dynamics simulation of the diffusion of /rparaffins In the Silicalite (a zeolite). A zig-zag pores B straight pores. From Runnebaum and Maginn [1997]. [Pg.192]


Since the development of grazing incidence x-ray diffraction, much of the convincing evidence for long-range positional order in layers has come from this technique. Structural relaxations from distorted hexagonal structure toward a relaxed array have been seen in heneicosanol [215]. Rice and co-workers combine grazing incidence x-ray diffraction with molecular dynamics simulations to understand several ordering transitions [178,215-219]. [Pg.135]

Small metal clusters are also of interest because of their importance in catalysis. Despite the fact that small clusters should consist of mostly surface atoms, measurement of the photon ionization threshold for Hg clusters suggest that a transition from van der Waals to metallic properties occurs in the range of 20-70 atoms per cluster [88] and near-bulk magnetic properties are expected for Ni, Pd, and Pt clusters of only 13 atoms [89] Theoretical calculations on Sin and other semiconductors predict that the stmcture reflects the bulk lattice for 1000 atoms but the bulk electronic wave functions are not obtained [90]. Bartell and co-workers [91] study beams of molecular clusters with electron dirfraction and molecular dynamics simulations and find new phases not observed in the bulk. Bulk models appear to be valid for their clusters of several thousand atoms (see Section IX-3). [Pg.270]

Bartell and co-workers have made significant progress by combining electron diffraction studies from beams of molecular clusters with molecular dynamics simulations [14, 51, 52]. Due to their small volumes, deep supercoolings can be attained in cluster beams however, the temperature is not easily controlled. The rapid nucleation that ensues can produce new phases not observed in the bulk [14]. Despite the concern about the appropriateness of the classic model for small clusters, its application appears to be valid in several cases [51]. [Pg.337]

It is possible to use the quantum states to predict the electronic properties of the melt. A typical procedure is to implement molecular dynamics simulations for the liquid, which pemiit the wavefiinctions to be detemiined at each time step of the simulation. As an example, one can use the eigenpairs for a given atomic configuration to calculate the optical conductivity. The real part of tire conductivity can be expressed as... [Pg.133]

Figure Al.3.30. Theoretical frequency-dependent conductivity for GaAs and CdTe liquids from ab initio molecular dynamics simulations [42]. Figure Al.3.30. Theoretical frequency-dependent conductivity for GaAs and CdTe liquids from ab initio molecular dynamics simulations [42].
Progress in the theoretical description of reaction rates in solution of course correlates strongly with that in other theoretical disciplines, in particular those which have profited most from the enonnous advances in computing power such as quantum chemistry and equilibrium as well as non-equilibrium statistical mechanics of liquid solutions where Monte Carlo and molecular dynamics simulations in many cases have taken on the traditional role of experunents, as they allow the detailed investigation of the influence of intra- and intemiolecular potential parameters on the microscopic dynamics not accessible to measurements in the laboratory. No attempt, however, will be made here to address these areas in more than a cursory way, and the interested reader is referred to the corresponding chapters of the encyclopedia. [Pg.832]

Specific solute-solvent interactions involving the first solvation shell only can be treated in detail by discrete solvent models. The various approaches like point charge models, siipennoleciilar calculations, quantum theories of reactions in solution, and their implementations in Monte Carlo methods and molecular dynamics simulations like the Car-Parrinello method are discussed elsewhere in this encyclopedia. Here only some points will be briefly mentioned that seem of relevance for later sections. [Pg.839]

Predicting the solvent or density dependence of rate constants by equation (A3.6.29) or equation (A3.6.31) requires the same ingredients as the calculation of TST rate constants plus an estimate of and a suitable model for the friction coefficient y and its density dependence. While in the framework of molecular dynamics simulations it may be worthwhile to numerically calculate friction coefficients from the average of the relevant time correlation fiinctions, for practical purposes in the analysis of kinetic data it is much more convenient and instructive to use experimentally detemiined macroscopic solvent parameters. [Pg.849]

Wang W, Nelson K A, Xiao L and Coker D F 1994 Molecular dynamics simulation studies of solvent cage effects on photodissociation in condensed phases J. Chem. Phys. 101 9663-71... [Pg.865]

Batista V S and Coker D F 1996 Nonadiabatic molecular dynamics simulation of photodissociation and geminate recombination of liquid xenon J. Chem. Phys. 105 4033-54... [Pg.865]

At any geometry g.], the gradient vector having components d EjJd Q. provides the forces (F. = -d Ej l d 2.) along each of the coordinates Q-. These forces are used in molecular dynamics simulations which solve the Newton F = ma equations and in molecular mechanics studies which are aimed at locating those geometries where the F vector vanishes (i.e. tire stable isomers and transition states discussed above). [Pg.2157]

Wilson M R 1997 Molecular dynamics simulations of flexible liquid crystal molecules using a Gay-Berne/Lennard-Jones model J. Chem. Phys. 107 8654-63... [Pg.2280]

Haile J M 1992 Molecular Dynamics Simulation Elementary Methods (New York Wiley)... [Pg.2281]

Rapaport D C 1995 The Art of Molecular Dynamics Simulation (Cambridge Cambridge University Press)... [Pg.2281]

SchlickT, Mandziuk M, Skeel R D and Srinivas K 1998 Nonlinear resonance artifacts in molecular dynamics simulations J. Comput. Phys. 140 1-29... [Pg.2281]

Ciccotti G and Ryckaert J P 1986 Molecular dynamics simulation of rigid molecules Comput. Phys. Rep. 4 345-92... [Pg.2281]

Procacci P, March M and Martyna G J 1998 Electrostatic calculations and multiple time scales in molecular dynamics simulation of flexible molecular systems J. Chem. Phys. 108 8799-803... [Pg.2282]

Andersen H C 1980 Molecular dynamics simulations at constant pressure and/or temperature J. Chem. [Pg.2282]

Tobias D J, Martyna G J and Klein M L 1993 Molecular dynamics simulations of a protein In the canonical ensemble J. Phys. Chem. 9712959-66... [Pg.2283]

Alejandre J, Tildesley D J and Chapela G A 1995 Molecular dynamics simulation of the orthobaric densities and surface tension of water J. Chem. Phys. 102 4574-83... [Pg.2288]

Holian B L and Lomdahl P S 1998 Plasticity induced by shockwaves in nonequilibrium molecular-dynamics simulations Soienoe 280 2085-8... [Pg.2289]

Hilbers P A J and Esselink K 1993 Parallel computing and molecular dynamics simulations Computer Simulation In Chemloal Physios /o 397 NATO ASI Series Ced M P Allen and D J Tlldesley (Dordrecht Kluwer) pp 473-95... [Pg.2290]

Niv M Y, Krylov A I and Gerber R B 1997 Photodissociation, electronic relaxation and recombination of HCI in Ar-n(HCI) clusters—non-adiabatic molecular dynamics simulations Faraday Discuss. Chem. Soc. 108 243-54... [Pg.2330]

Sokal A D 1995 Monte Carlo and Molecular Dynamics Simulations in Polymer Science ed K Binder (New York Oxford University Press) oh 3... [Pg.2384]

Kremer K and Grest G S 1990 Dynamics of entangled linear polymer melts a molecular-dynamics simulation J Chem. Phys. 92 5057... [Pg.2384]

Figure C2.3.7. Snapshot of micelle of sodium octanoate obtained during molecular dynamics simulation. The darkest shading is for sodium counter-ions, the lightest shading is for oxygens and the medium shading is for carbon atoms. Reproduced by pennission from figure 2 of [36]. Figure C2.3.7. Snapshot of micelle of sodium octanoate obtained during molecular dynamics simulation. The darkest shading is for sodium counter-ions, the lightest shading is for oxygens and the medium shading is for carbon atoms. Reproduced by pennission from figure 2 of [36].
Weakliem P C and Carter E A 1993 Surface chemical reactions studied via ab /n/f/o-derived molecular dynamics simulations fluorine etching of Si(IOO) J. Chem Phys. 98 737-45... [Pg.2942]

Barone M E and Graves D B 1995 Molecular dynamics simulations of direct reactive ion etching of silicon by fluorine and chlorine J. Appi. Phys. 78 6604-15... [Pg.2942]

Helmer B A and Graves D B 1997 Molecular dynamics simulations of fluorosllyl Ions with silicon J. Vac. Sc/. Technol. A 15 2252-61... [Pg.2943]

Hanson D E, Voter A F and Kress J D 1997 Molecular dynamics simulation of reactive Ion etching of SI by energetic Cl Ions J. Appl. Phys. 82 3552-9... [Pg.2943]


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0-value analysis molecular dynamics simulation

Ab Initio Molecular-Dynamics Simulations of Doped Phase-Change Materials

Acetonitrile molecular dynamics simulation

All-atom molecular dynamics simulations

Amphiphiles, molecular dynamics simulation

Aqueous solutions molecular dynamics simulations

Atom motions molecular-dynamics simulations

Atomistic simulation molecular dynamics

Basic Techniques of Monte Carlo and Molecular Dynamics Simulation

Bentonite molecular dynamics simulation

Bilayer molecular dynamics simulation

Biological enzyme modeling molecular dynamics simulations

Born-Oppenheimer molecular dynamics simulations

Boundary conditions, molecular dynamics simulations

Calculation theory, molecular dynamics simulation

Canonical ensemble molecular dynamics simulations

Car-Parrinello molecular dynamics simulation

Carbohydrates, molecular dynamics simulations

Carbon nanotubes molecular dynamic simulations

Cartesian coordinates molecular dynamics simulation

Challenges in Molecular Dynamics Simulations of Multicomponent Oxide Glasses

Coarse-grained molecular dynamics CGMD) simulations

Computational studies molecular dynamics simulations

Computer simulation molecular dynamics

Computer simulation molecular dynamics method

Computer simulations of molecular dynamics

Computer-simulated molecular dynamics

Contact interactions molecular dynamic simulation

Crystal growth direct molecular dynamic simulations

Denatured state molecular dynamics simulation

Digital simulation molecular dynamics

Dynamic simulation

Dynamical simulations

Enzyme catalysis molecular dynamics simulation

Examples of Molecular Dynamics Simulations

Experimental procedure molecular dynamics simulation

Exponential model molecular dynamics simulation

First principle molecular dynamics FPMD) simulations

First principles molecular dynamics simulations of

First-principles molecular dynamics simulations

Fluid density, molecular dynamics simulations

Fluid molecular dynamics simulations

Force Fields and Molecular Dynamics Simulations

Force probe molecular dynamics simulations

Free energy simulations, types molecular dynamics

Glass structures molecular dynamics simulations

Grain molecular dynamics simulations

Grained Molecular Dynamics Simulations

Helix motions molecular dynamics simulation

Interfacial electrochemical processes molecular dynamics simulation

Ionic molecular dynamics simulations

Lennard-Jones interactions molecular dynamics simulation

Lennard-Jones parameters used molecular dynamics simulations

Lennard-Jones potential molecular dynamics simulation

Lipid bilayers molecular dynamics simulation

Liquid media molecular dynamics simulations

Long molecular dynamics simulations

Lysozyme molecular dynamics simulation

Membrane molecular dynamics simulation

Methane molecular dynamics simulation

Micelle formation molecular dynamics simulation

Molecular Dynamics (MD) Simulations

Molecular Dynamics Simulation and Homogenization Analysis

Molecular Dynamics Simulations Electrokinetic Nanofluidics

Molecular Dynamics Simulations of Amorphous Systems

Molecular Dynamics or Monte Carlo simulations

Molecular dynamic simulation amorphous ices

Molecular dynamic simulation atomic motion

Molecular dynamic simulation solid-state studies

Molecular dynamic simulation studies

Molecular dynamic simulation transformations

Molecular dynamic simulations barrier crossing

Molecular dynamic simulations hydrogen bonds

Molecular dynamic simulations protein flexibility

Molecular dynamic simulations statistical mechanical

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Molecular dynamic simulations with docking methods

Molecular dynamics and Monte Carlo simulations

Molecular dynamics extracting information from simulation

Molecular dynamics physical simulation

Molecular dynamics simulation 3 " -order

Molecular dynamics simulation Monte Carlo compared with

Molecular dynamics simulation advantage

Molecular dynamics simulation algorithms

Molecular dynamics simulation anharmonic contributions

Molecular dynamics simulation bead-spring model

Molecular dynamics simulation bilayers

Molecular dynamics simulation biomolecules

Molecular dynamics simulation calculation techniques

Molecular dynamics simulation coarse-grained

Molecular dynamics simulation comparison with experiment

Molecular dynamics simulation computational chemistry

Molecular dynamics simulation conformational analysis

Molecular dynamics simulation conformational changes from

Molecular dynamics simulation continuous methods

Molecular dynamics simulation coordinated metal ions

Molecular dynamics simulation description

Molecular dynamics simulation different techniques

Molecular dynamics simulation dipalmitoylphosphatidylcholine

Molecular dynamics simulation dynamical properties

Molecular dynamics simulation electrolytes

Molecular dynamics simulation energy conservation

Molecular dynamics simulation ensemble

Molecular dynamics simulation explicit solvent models

Molecular dynamics simulation features

Molecular dynamics simulation finite-field

Molecular dynamics simulation force field

Molecular dynamics simulation free energy calculations

Molecular dynamics simulation free energy perturbation

Molecular dynamics simulation glass transition

Molecular dynamics simulation indirect technique

Molecular dynamics simulation interaction potentials

Molecular dynamics simulation ionic liquids

Molecular dynamics simulation limitations

Molecular dynamics simulation liquid

Molecular dynamics simulation liquid water

Molecular dynamics simulation method

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Molecular dynamics simulation modelling

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Molecular dynamics simulation nucleic acid systems

Molecular dynamics simulation nucleic acids

Molecular dynamics simulation of enzymes

Molecular dynamics simulation of simple fluids

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Molecular dynamics simulation proteins

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Molecular dynamics simulation solute-solvent interactions

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Molecular dynamics simulation supercritical aqueous solutions

Molecular dynamics simulation supercritical water

Molecular dynamics simulation theory

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Molecular dynamics simulation time-dependent properties

Molecular dynamics simulation ubiquitin

Molecular dynamics simulation unrestrained

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Molecular dynamics simulation with periodic boundary conditions

Molecular dynamics simulation with stochastic boundary conditions

Molecular dynamics simulation, diffusion

Molecular dynamics simulation, diffusion coefficient estimation

Molecular dynamics simulation, vibrational line

Molecular dynamics simulations Monte Carlo

Molecular dynamics simulations Subject

Molecular dynamics simulations applications

Molecular dynamics simulations background

Molecular dynamics simulations bonded interactions

Molecular dynamics simulations cation

Molecular dynamics simulations electrode-electrolyte interface

Molecular dynamics simulations electrostatic free energies

Molecular dynamics simulations explicit solvent simulation

Molecular dynamics simulations extended Lagrangian method

Molecular dynamics simulations field—parameterization

Molecular dynamics simulations framework

Molecular dynamics simulations implicit solvation model

Molecular dynamics simulations ionic fluids

Molecular dynamics simulations kinetic theory

Molecular dynamics simulations mean-field theories

Molecular dynamics simulations mechanical scheme

Molecular dynamics simulations memory requirements

Molecular dynamics simulations nonbonded interactions

Molecular dynamics simulations of Li ion and H-conduction in polymer electrolytes

Molecular dynamics simulations of interfacial

Molecular dynamics simulations of membranes

Molecular dynamics simulations of peptides

Molecular dynamics simulations of proton

Molecular dynamics simulations of proton transport

Molecular dynamics simulations oscillatory force

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Molecular dynamics simulations simulated time trajectory

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Molecular dynamics simulations using induced dipoles

Molecular dynamics simulations with polarizable force fields

Molecular dynamics simulations, Plate

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Molecular dynamics type simulations

Molecular dynamics/simulation quantum chemical calculations

Molecular modeling energy minimization, dynamics simulation

Molecular modelling dynamic simulation models

Molecular simulations

Nickel molecular dynamics simulations

Non-equilibrium Molecular Dynamics Simulations of Coarse-Grained Polymer Systems

PRISM theory molecular dynamics simulations

Pancreatic trypsin inhibitor, molecular dynamics simulation

Peptides classical molecular dynamics simulations

Phosphatidylcholine bilayer molecular dynamics simulation

Plasma processing molecular dynamics simulations

Polymers molecular dynamics simulation

Polystyrene molecular dynamics simulation

Position-dependent rate molecular dynamics simulation

Potential molecular dynamics simulation

Pressure molecular dynamic simulation

Reactive molecular dynamics simulations

Relaxation time molecular dynamics simulation

Setting up and Running a Molecular Dynamics Simulation

Simulated Annealing by Molecular Dynamics Simulation in Cartesian Space

Simulated annealing molecular dynamics simulation

Simulated monolayers molecular dynamics

Simulation from molecular dynamics

Simulation from molecular dynamics trajectories

Simulation of protein molecular dynamics

Simulations, molecular dynamics PDMS)

Single molecular dynamic simulations

Smooth surfaces molecular dynamic simulations

Solvation/solvents molecular dynamics simulation

Solvent Models in Molecular Dynamics Simulations A Brief Overview

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Statistical simulations molecular dynamics

Steered molecular dynamics simulations

Stochastic boundary molecular dynamics simulations

Structural Insight into Transition Metal Oxide Containing Glasses by Molecular Dynamic Simulations

Sucrose molecular dynamics simulations

Surface force molecular dynamic simulation

Surface force molecular dynamic simulation, wetting

Temperature molecular dynamics simulation

Tight binding molecular dynamics simulation

Time scales molecular dynamics simulations, protein

Time, molecular dynamics simulations

Vacuum molecular dynamics simulation

Vacuum molecular dynamics simulation energy calculations

Vapor growth, molecular dynamics simulations

Zeolite diffusion, simulations molecular dynamics

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