Free energy estimation (Monte Carlo)

In summary, there are many resources available to help you understand the algorithm for free energy estimation using Monte Carlo methods, including books, tutorials, and online resources. However, there is not a readily available source code for these methods.
  • #1
Machiveli
2
0
Can anyone point me to some source code for monte carlo methods for free energy estimation. Standard metropolis/hamiltonian don't work since its the integral not the expectation I'm interested in.

candidates seem to be path sampling/ bridge sampling and annealed importance sampling of which anealed importance sampling seems to be the best.

However I can't quite understand the algorithm from Neal's paper
http://www.cs.toronto.edu/~radford/ais-pub.abstract.html

So does anyone know of any source code/pseudo code or even just a good expination of what the algorithm is?
 
Physics news on Phys.org
  • #2
Unfortunately, there isn't any easy to find source code for these methods. However, there are several books and tutorials that can provide you with a good explanation of the algorithm, as well as detailed descriptions of the various techniques used in Monte Carlo simulations. One of the best books on the subject is "Monte Carlo Methods in Statistical Physics" by M.E.J. Newman and G.T. Barkema. This book provides a comprehensive introduction to the field, and covers many of the topics related to free energy estimation using Monte Carlo techniques. Another good tutorial is "Introduction to Monte Carlo Methods" by S.K. Nandi, which provides a more concise overview of the various Monte Carlo methods used in free energy estimation. Finally, if you're looking for something more hands-on, you can always try implementing your own Monte Carlo simulation from scratch. There are several online tutorials which provide step-by-step instructions on how to set up and run a Monte Carlo simulation.
 

1. What is free energy estimation in Monte Carlo simulations?

Free energy estimation in Monte Carlo simulations is a method used to calculate the free energy of a system using statistical mechanics and computer simulations. It involves sampling the possible configurations of a system and using these samples to calculate the free energy.

2. Why is free energy estimation important in Monte Carlo simulations?

Free energy estimation is important in Monte Carlo simulations because it allows for the prediction of the thermodynamic properties of a system, such as its enthalpy, entropy, and free energy. These properties are crucial in understanding the behavior of a system and its phase transitions.

3. How is free energy estimated in Monte Carlo simulations?

Free energy is estimated in Monte Carlo simulations by using statistical mechanics and the Metropolis algorithm. First, a large number of configurations of the system are sampled and the probabilities of each configuration are calculated. Then, the free energy is calculated using the Boltzmann distribution and the probabilities of each configuration.

4. What are the limitations of free energy estimation in Monte Carlo simulations?

There are several limitations to free energy estimation in Monte Carlo simulations, including inaccuracies due to finite sampling, difficulties in sampling rare events, and the assumption of equilibrium conditions. Additionally, the accuracy of the estimation depends on the accuracy of the force field and potential energy parameters used in the simulation.

5. How can the accuracy of free energy estimation be improved in Monte Carlo simulations?

The accuracy of free energy estimation in Monte Carlo simulations can be improved by increasing the number of sample configurations, using more accurate force fields and potential energy parameters, and using advanced techniques such as umbrella sampling or adaptive biasing force methods. It is also important to carefully analyze the results and consider the limitations and assumptions of the method.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
12
Views
2K
Replies
0
Views
2K
  • MATLAB, Maple, Mathematica, LaTeX
Replies
2
Views
1K
  • Atomic and Condensed Matter
Replies
3
Views
864
  • Nuclear Engineering
Replies
4
Views
3K
  • Programming and Computer Science
Replies
4
Views
8K
  • Atomic and Condensed Matter
Replies
4
Views
3K
Replies
1
Views
4K
Back
Top