Inverse Monte Carlo: Difference between revisions

From SklogWiki
Jump to navigation Jump to search
mNo edit summary
 
(16 intermediate revisions by 4 users not shown)
Line 1: Line 1:
* Inverse Monte Carlo refers to the numerical techniques to solve the
'''Inverse Monte Carlo''' refers to the numerical techniques to solve the so-called inverse problem in fluids.
so-called inverse problem in fluids.
Given the structural information (distribution functions) the inverse [[Monte Carlo |Monte Carlo technique]] tries to compute the corresponding [[Intermolecular pair potential |interaction potential]].
More information can be found in the review by Gergely Tóth (see reference 4).
== An inverse Monte Carlo algorithm using a [[Wang-Landau method|Wang-Landau]]-like algorithm ==
A detailed explanation of the procedure can be found in reference 1. Here an outline  description for a simple fluid system is given:
==== Input information ====
#The experimental [[radial distribution function |radial distribution function]] <math> g_0(r) </math> at given conditions of [[temperature]], <math> T </math>  and [[density]] <math> \rho </math>
#An initial guess for the effective interaction [[Intermolecular pair potential |(pair) potential]], i.e.
::<math> \beta \Phi_{12} (r) \equiv \frac{ \Phi_{12}(r) }{ k_B T} </math>


* Given the structural information (distribution functions) the inverse Monte
==== Procedure ====
Carlo technique tries to compute the corresponding interaction potential
The simulation procedure is divided into several stages. First, simulations are performed to modify the  effective interaction at each stage, <math> s </math>, in order  to bias the
the radial distribution function, <math> g_{inst}(r) </math> towards the target <math> g_0(r) </math>  by using:


* Some example(s) of these techniques can be found in  the following reference(s)
: <math> \beta \Phi_{12}^{new}(r) = \beta \Phi_{12}^{old}(r) + \left[  g_{\mathrm{inst}}(r) - g_0(r) \right] \lambda_s </math>,


== References ==
where <math> \lambda_s </math> is greater than zero and depends on the stage <math> s </math> at which one is at.
The simulation for each stage proceeds until some convergence criteria (that takes into account
the precision  of the values of <math> g_0(r) </math>) for the global result of the
radial distribution function over the stage,  is achieved (See Ref. 1))
When the simulation for a particular stage have finished a new stage is initiated, with a smaller value of <math> \lambda </math>:
 
: <math> \left. \lambda_{s+1} \right.= \alpha \lambda_s </math> with: <math>  0 < \alpha < 1 </math>


#[http://dx.doi.org/10.1103/PhysRevE.68.011202 N. G. Almarza and E. Lomba, "Determination of the interaction potential from the pair distribution function: An inverse Monte Carlo technique", Phys. Rev. E 68, 011202 (2003) (6 pages)]
At the final stage, with a sufficiently small <math> \lambda </math>, one can obtain an effective pair potential compatible with the input radial distribution function <math> g_0(r) </math>. One knows that this effective pair potential is valid due to the uniqueness theorem of Henderson (Ref. 3).


#[http://dx.doi.org/10.1103/PhysRevE.70.021203  N. G. Almarza, E. Lomba, and D. Molina. "Determination of effective pair interactions from the structure factor", Phys. Rev. E 70, 021203 (2004) (5 pages)  ]
== References ==
#[http://dx.doi.org/10.1103/PhysRevE.68.011202 N. G. Almarza and E. Lomba, "Determination of the interaction potential from the pair distribution function: An inverse Monte Carlo technique", Physical Review E '''68''' 011202 (6 pages) (2003)]
#[http://dx.doi.org/10.1103/PhysRevE.70.021203  N. G. Almarza, E. Lomba, and D. Molina. "Determination of effective pair interactions from the structure factor", Physical Review E '''70''' 021203 (5 pages) (2004)]
#[http://dx.doi.org/10.1016/0375-9601(74)90847-0 R. L. Henderson  "A uniqueness theorem for fluid pair correlation functions", Physics Letters A  '''49''' pp. 197-198 (1974)]
#[http://dx.doi.org/10.1088/0953-8984/19/33/335220  Gergely Tóth, "Interactions from diffraction data: historical and comprehensive overview of simulation assisted methods", Journal of Physics: Condensed Matter '''19''' 335220 (2007)]
[[category: Monte Carlo]]

Latest revision as of 12:51, 6 September 2007

Inverse Monte Carlo refers to the numerical techniques to solve the so-called inverse problem in fluids. Given the structural information (distribution functions) the inverse Monte Carlo technique tries to compute the corresponding interaction potential. More information can be found in the review by Gergely Tóth (see reference 4).

An inverse Monte Carlo algorithm using a Wang-Landau-like algorithm[edit]

A detailed explanation of the procedure can be found in reference 1. Here an outline description for a simple fluid system is given:

Input information[edit]

  1. The experimental radial distribution function at given conditions of temperature, and density
  2. An initial guess for the effective interaction (pair) potential, i.e.

Procedure[edit]

The simulation procedure is divided into several stages. First, simulations are performed to modify the effective interaction at each stage, , in order to bias the the radial distribution function, towards the target by using:

,

where is greater than zero and depends on the stage at which one is at. The simulation for each stage proceeds until some convergence criteria (that takes into account the precision of the values of ) for the global result of the radial distribution function over the stage, is achieved (See Ref. 1)) When the simulation for a particular stage have finished a new stage is initiated, with a smaller value of :

with:

At the final stage, with a sufficiently small , one can obtain an effective pair potential compatible with the input radial distribution function . One knows that this effective pair potential is valid due to the uniqueness theorem of Henderson (Ref. 3).

References[edit]

  1. N. G. Almarza and E. Lomba, "Determination of the interaction potential from the pair distribution function: An inverse Monte Carlo technique", Physical Review E 68 011202 (6 pages) (2003)
  2. N. G. Almarza, E. Lomba, and D. Molina. "Determination of effective pair interactions from the structure factor", Physical Review E 70 021203 (5 pages) (2004)
  3. R. L. Henderson "A uniqueness theorem for fluid pair correlation functions", Physics Letters A 49 pp. 197-198 (1974)
  4. Gergely Tóth, "Interactions from diffraction data: historical and comprehensive overview of simulation assisted methods", Journal of Physics: Condensed Matter 19 335220 (2007)