Binder The Monte Carlo Method in Condensed Matter Physics

Erscheinungsjahr 2012
ISBN: 978-3-662-02855-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 71, 393 Seiten, eBook

Reihe: Topics in Applied Physics

The Monte Carlo method is now widely used and commonly accepted as an important and useful tool in solid state physics and related fields. It is broadly recognized that the technique of "computer simulation" is complementary to both analytical theory and experiment, and can significantly contribute to ad vancing the understanding of various scientific problems. Widespread applications of the Monte Carlo method to various fields of the statistical mechanics of condensed matter physics have already been reviewed in two previously published books, namely Monte Carlo Methods in Statistical Physics (Topics Curro Phys. , Vol. 7, 1st edn. 1979, 2ndedn. 1986) and Applications of the Monte Carlo Method in Statistical Physics (Topics Curro Phys. , Vol. 36, 1st edn. 1984, 2nd edn. 1987). Meanwhile the field has continued its rapid growth and expansion, and applications to new fields have appeared that were not treated at all in the above two books (e. g. studies of irreversible growth phenomena, cellular automata, interfaces, and quantum problems on lattices). Also, new methodic aspects have emerged, such as aspects of efficient use of vector com puters or parallel computers, more efficient analysis of simulated systems con figurations, and methods to reduce critical slowing down at i>hase transitions. Taken together with the extensive activity in certain traditional areas of research (simulation of classical and quantum fluids, of macromolecular materials, of spin glasses and quadrupolar glasses, etc.

Zielgruppe

Research

Autoren/Hrsg.

Binder, Kurt

Weitere Mitwirkende

Baumgärtner, A.

Binder, Kurt

Burkitt, A.N.

Ceperley, D.M.

De Raedt, H.

Ferrenberg, A.M.

Heermann, D.W.

Herrmann, H.J.

Landau, D.P.

Levesque, D.

Linden, W. von der

Reger, J.D.

Schmidt, K.E.

Selke, W.

Stauffer, D.

Swendsen, R.H.

Wang, J.-S.

Weis, J.J.

Young, A.P.

Inhaltsverzeichnis

1. Introduction.- 1.1 General Remarks.- 1.2 Progress in the Understanding of Finite Size Effects at Phase Transitions.- 1.3 Statistical Errors.- 1.4 Final Remarks.- References.- 2. Vectorisation of Monte Carlo Programs for Lattice Models Using Supercomputers.- 2.1 Introduction.- 2.2 Technical Details.- 2.3 Simple Vectorisation Algorithms.- 2.4 Vectorised Multispin Coding Algorithms.- 2.5 Vectorised Multilattice Coding Algorithms.- 2.6 Vectorised Microcanonical Algorithms.- 2.7 Some Recent Results from Vectorised Algorithms.- 2.8 Conclusion.- References.- 3. Parallel Algorithms for Statistical Physics Problems.- 3.1 Paradigms of Parallel Computing.- 3.2 Applications on Fine-Grained SIMD Machines.- 3.3 Applications on Coarse-Grained MIMD Machines.- 3.4 Prospects.- References.- 4. New Monte Carlo Methods for Improved Efficiency of Computer Simulations in Statistical Mechanics.- 4.1 Overview.- 4.2 Acceleration Algorithms.- 4.3 Histogram Methods.- 4.4 Summary.- References.- 5. Simulation of Random Growth Processes.- 5.1 Irreversible Growth of Clusters.- 5.2 Reversible Probabilistic Growth.- 5.3 Conclusion.- References.- 6. Recent Progress in the Simulation of Classical Fluids.- 6.1 Improvements of the Monte Carlo Method.- 6.2 Pure Phases and Mixtures of Simple Fluids.- 6.3 Coulombic and Ionic Fluids.- 6.4 Simulations of Inhomogeneous Simple Fluids.- 6.5 Molecular Liquids: Model Systems.- 6.6 Molecular Liquids: Realistic Systems.- 6.7 Solutions.- 6.8 Interfaces in Molecular Systems.- References.- 7. Monte Carlo Techniques for Quantum Fluids, Solids and Droplets.- 7.1 Variational Method.- 7.2 Green’s Function Monte Carlo and Related Methods.- 7.3 Path Integral Monte Carlo Method.- 7.4 Some Results for Bulk Helium.- 7.5 Momentum and Related Distributions.- 7.6 Droplets andSurfaces.- 7.7 Future Prospects.- References.- 8. Quantum Lattice Problems.- 8.1 Overview.- 8.2 Models.- 8.3 Variational Monte Carlo Method.- 8.4 Green’s Function Monte Carlo Method.- 8.5 Grand Canonical Quantum Monte Carlo Method.- 8.6 Projector Quantum Monte Carlo Method.- 8.7 Fundamental Difficulties.- 8.8 Concluding Remarks.- 8.A Appendix.- References.- 9. Simulations of Macromolecules.- 9.1 Techniques and Models.- 9.2 Amorphous Systems.- 9.3 Disorder Effects.- 9.4 Mesomorphic Systems.- 9.5 Networks.- 9.6 Segregation.- 9.7 Surfaces and Interfaces.- 9.8 Special Polymers.- References.- 10. Percolation, Critical Phenomena in Dilute Magnets, Cellular Automata and Related Problems.- 10.1 Percolation.- 10.2 Dilute Ferromagnets.- 10.3 Cellular Automata.- 10.4 Multispin Programming of Cellular Automata.- 10.5 Kauffman Model and da Silva—Herrmann Algorithm.- References.- 11. Interfaces, Wetting Phenomena, Incommensurate Phases.- 11.1 Interfaces in Ising Models.- 11.2 Interfaces in Multistate Models.- 11.3 Dynamical Aspects.- 11.4 Spatially Modulated Structures.- 11.5 Conclusions.- References.- 12. Spin Glasses, Orientational Glasses and Random Field Systems.- 12.1 Spin Glasses.- 12.2 Potts Glasses.- 12.3 Orientational Glasses.- 12.4 The Random-Field Ising Model.- 12.5 Concluding Remarks and Outlook.- References.