The Monte Carlo Method in Condensed Matter 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 c...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
1992, 1992
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| Edition: | 1st ed. 1992 |
| Series: | Topics in Applied Physics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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