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[edit]
General reading[edit]
- 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)