Reverse Monte Carlo: Difference between revisions
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Reverse Monte Carlo (RMC) [1] is a variation of the standard Metropolis Monte Carlo (MMC) method. It is used to produce a 3 dimensional atomic model that fits a set of measurements (Neutron-, X-ray-diffraction, EXAFS etc.). | Reverse Monte Carlo (RMC) [1] is a variation of the standard [[Metropolis Monte Carlo]] (MMC) method. It is used to produce a 3 dimensional atomic model that fits a set of measurements (Neutron-, X-ray-diffraction, EXAFS etc.). | ||
In addition to measured data a number of constraints based on prior knowledge of the system (like chemical bonds etc.) can be applied. Some examples are: | In addition to measured data a number of constraints based on prior knowledge of the system (like chemical bonds etc.) can be applied. Some examples are: | ||
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#Calculate the total radial distribution function <math>g_o^C(r)</math> for this old configuration. | #Calculate the total radial distribution function <math>g_o^C(r)</math> for this old configuration. | ||
#Transform to the total structure factor: | #Transform to the total structure factor: | ||
<math>S_o^2 (Q)-1=\frac{4\pi\rho}{Q}\int\limits_{0}^{\infty} r(g_o^C(r)-1)\sin(Qr)\, dr</math> | #:<math>S_o^2 (Q)-1=\frac{4\pi\rho}{Q}\int\limits_{0}^{\infty} r(g_o^C(r)-1)\sin(Qr)\, dr</math> | ||
#:where ''Q'' is the momentum transfer <math>\rho</math> and the number density. | |||
where ''Q'' is the momentum transfer <math>\rho</math> and the number density. | |||
#Calculate the difference between the measured structure factor <math>S^E(Q)</math> and the one calculated from the configuration <math>S_o^C(Q)</math>: | #Calculate the difference between the measured structure factor <math>S^E(Q)</math> and the one calculated from the configuration <math>S_o^C(Q)</math>: | ||
<math>\chi_o^2=\sum_i(S_o^C(Q_i)-S^E(Q_i))^2/\sigma(Q_i)^2</math> | #:<math>\chi_o^2=\sum_i(S_o^C(Q_i)-S^E(Q_i))^2/\sigma(Q_i)^2</math> | ||
this sum is taken over all experimental points <math>\sigma</math> is the experimental error. | #:this sum is taken over all experimental points <math>\sigma</math> is the experimental error. | ||
#Select and move one atom at random and calculate the new distribution function, structure factor and: | #Select and move one atom at random and calculate the new distribution function, structure factor and: | ||
<math>\chi_n^2=\sum_i(S_n^C(Q_i)-S^E(Q_i))^2/\sigma(Q_i)^2</math> | #:<math>\chi_n^2=\sum_i(S_n^C(Q_i)-S^E(Q_i))^2/\sigma(Q_i)^2</math> | ||
#If <math>\chi_n^2<\chi_o^2</math> accept the move and let the new configuration become the old. If <math>\chi_n^2>\chi_o^2</math> then the move is accepted with probability <math>\exp(-(\chi_n^2-\chi_0^2)/2)</math> otherwiase rejected. | #If <math>\chi_n^2<\chi_o^2</math> accept the move and let the new configuration become the old. If <math>\chi_n^2>\chi_o^2</math> then the move is accepted with probability <math>\exp(-(\chi_n^2-\chi_0^2)/2)</math> otherwiase rejected. | ||
#repeat from step 5. | #repeat from step 5. | ||
Revision as of 19:12, 19 February 2007
Reverse Monte Carlo (RMC) [1] is a variation of the standard Metropolis Monte Carlo (MMC) method. It is used to produce a 3 dimensional atomic model that fits a set of measurements (Neutron-, X-ray-diffraction, EXAFS etc.). In addition to measured data a number of constraints based on prior knowledge of the system (like chemical bonds etc.) can be applied. Some examples are:
- Closest approach between atoms (hard sphere potential)
- Coordination numbers.
- Angles in triplets of atoms.
The algorithm for RMC can be written:
- Start with a configuration of atoms with periodic boundary conditions. This can be a random or a crystalline configuration from a different simulation or model.
- Calculate the total radial distribution function for this old configuration.
- Transform to the total structure factor:
- where Q is the momentum transfer and the number density.
- Calculate the difference between the measured structure factor and the one calculated from the configuration :
- this sum is taken over all experimental points is the experimental error.
- Select and move one atom at random and calculate the new distribution function, structure factor and:
- If accept the move and let the new configuration become the old. If then the move is accepted with probability otherwiase rejected.
- repeat from step 5.
When have reached an equilibrium the configuration is saved and can be analysed.
