Monte Carlo

Monte Carlo is a stochastic computer simulation technique frequently used in the study of soft matter.

• Basin-hopping Monte Carlo
• Cluster algorithms
• Concerted rotation algorithm
• Configurational bias Monte Carlo
• Constant-pressure Monte Carlo
• Detailed balance
• End-bridging Monte Carlo
• Fragment regrowth Monte Carlo
• Gibbs-Duhem integration
• Gibbs ensemble Monte Carlo
• Glauber transition probabilities also known as: Barkers method
• Grand-canonical Monte Carlo
• Histogram reweighting
• Importance sampling
• Inverse Monte Carlo
• Kinetic Monte Carlo
• Lattice simulations (Polymers)
• Markov chain
• Mayer sampling Monte Carlo
• Metropolis Monte Carlo
• Metropolis-Hastings Monte Carlo
• Monte Carlo in the microcanonical ensemble
• Monte Carlo reptation moves
• Overlapping distribution method
• Parrinello-Rahman barostat
• Phase switch Monte Carlo
• Quantum Monte Carlo
• Random numbers
• Recoil growth
• Reverse Monte Carlo
• RIS Metropolis Monte Carlo
• Simulated annealing
• Tethered Monte Carlo
• Umbrella sampling
• Wang-Landau method
• Waste recycling Monte Carlo

Historical papers

• Nicholas Metropolis and S. Ulam "The Monte Carlo Method", Journal of the American Statistical Association 44 pp. 335-341 (1949)

General reading

• M. P. Allen and D. J. Tildesley "Computer Simulation of Liquids", Oxford University Press (1989) Chapter 4.
• Daan Frenkel and Berend Smit "Understanding Molecular Simulation: From Algorithms to Applications", Second Edition (2002) ISBN 0-12-267351-4 Chapter 3.
• Daan Frenkel "Introduction to Monte Carlo Methods", in Computational Soft Matter: From Synthetic Polymers to Proteins, NIC Series Volume 23 (2004)
• David P. Landau and Kurt Binder "A Guide to Monte Carlo Simulations in Statistical Physics", 2nd Edition, Cambridge University Press (2005)