Computer Simulation in Chemical Physics

1 The Monte Carlo Method.- 1.1 The Metropolis algorithm.- 1.2 Other ensembles.- 1.3 A non-symmetrical underlying matrix.- 1.4 Molecular systems.- 1.5 Polymers.- 1.6 Conclusions.- 2 The Molecular Dynamics Method.- 2.1 Introduction.- 2.2 Constraint dynamics.- 2.3 Long-range forces.- 2.4 Conclusions.- 3 Back to basics.- 3.1 Introduction.- 3.2 Average values, fluctuations and errors.- 3.3 Ensembles and distributions.- 3.4 Distribution functions.- 3.5 Finite-size scaling and phase transitions.- 3.6 Transport coefficients.- 3.7 Conclusions.- 4 Advanced Monte Carlo Techniques.- 4.1 Introduction.- 4.2 Other ensembles.- 4.3 Virtual moves.- 4.4 Beyond single-particle moves.- 4.5 Thermodynamic integration.- 4.6 The grand canonical miracle.- 5 Thermodynamic Constraints.- 5.1 Introduction.- 5.2 Andersen’s demon.- 5.3 Time and ensemble averages.- 5.4 Nosé-Hoover thermostat.- 5.5 Molecular systems.- 5.6 Numerical considerations.- 6 Computer Simulations in the Gibbs Ensemble.- 6.1 Introduction.- 6.2 Theoretical aspects.- 6.3 Computational aspects.- 6.4 Finite-size effects.- 6.5 Analyzing the results.- 6.6 Applications.- 7 Effective Pair Potentials and Beyond.- 7.1 Introduction.- 7.2 Where potentials come from.- 7.3 Simple atomic and ionic systems.- 7.4 Molecular liquids and solids.- 7.5 Introducing electronic polarization.- 7.6 Bonded interactions.- 7.7 Conclusion.- 8 First-Principles Molecular Dynamics.- 8.1 Introduction.- 8.2 First-principles interatomic potential.- 8.3 Molecular dynamics based on density functional theory.- 8.4 Practical implementation of a DF-MD scheme.- 8.5 Selected applications of first-principles molecular dynamics.- 9 Computer Simulation Methods for Nonadiabatic Dynamics in Condensed Systems.- 9.1 Introduction.- 9.2 Formal background and basic principles.-9.3 Surface-hopping trajectory methods.- 9.4 Self-consistent energy-conserving nonadiabatic dynamics.- 9.5 Test problems and applications.- 9.6 Conclusions.- 10 Long Length-Scale Aspects of Self Organization Phenomena.- 10.1 Introduction: an apologia.- 10.2 Universality and near universality.- 10.3 The nearest-neighbour Ising model as a computational model of simple fluids.- 10.4 Wheeler-Widom-type models as lattice fluids.- 10.5 Some tricks of the trade; Ising-like models.- 10.6 Analysis of Monte-Carlo data: finite-size scaling in Monte-Carlo simulations.- 10.7 Equilibrium simulation of Ising lattice model of microemulsion and the systematic construction of the phase diagram of microemulsion.- 10.8 Conclusions.- 11 Computer Simulation of Polymers.- 11.1 Introduction.- 11.2 Polymer simulations: general considerations.- 11.3 Algorithms for static properties.- 11.4 Simulations of polymer dynamics.- 11.5 Reptation.- 11.6 Extensions: glasses and networks.- 11.7 Further reading.- 11.8 Conclusions.- 12 Computer Simulations of Surfactants.- 12.1 Introduction.- 12.2 Surfactant monolayers.- 12.3 Micelles.- 12.4 Concluding remarks.- 13 Parallel Computing and Molecular Dynamics Simulations.- 13.1 Introduction.- 13.2 Computer architectures.- 13.3 Parallel programming concepts.- 13.4 Parallel molecular dynamics.- 13.5 Nucleation simulations.- 13.6 Conclusion.- 14 Scientific Visualization, A User View.- 14.1 Introduction.- 14.2 Discussion.- List of Participants.- List of Posters.