Reverse Monte Carlo

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 chemocal bonds etc.) can be applied. Some examples are:

1. Closest approach between atoms (hard sphere potential)
2. Coordination numbers.
3. Angels in triplets of atoms.

The algorithm for RMC can be written:

1. 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.
2. Calculate the total radial distribution function $g_{o}^{C}(r)$ for this old configuration.
3. Transform to the total structure factor:

$S_{o}^{2}(Q)-1={\frac {4\pi \rho }{Q}}\int \limits _{0}^{\inf }r(g_{o}^{C}(r)-1)sin(Qr)\,dr$

where Q is the momentum transfer $\rho$ and the number density.

1. Calculate the difference between the measured structure factor $S^{E}(Q)$ and the one calculated from the configuration $S_{o}^{C}(Q)$:

$\chi _{o}^{2}=\sum _{i}(S_{o}^{C}(Q_{i})-S^{E}(Q_{i}))^{2}/\sigma (Q_{i})^{2}$

this sum is taken over all experimental points $\sigma$ is the experimental error.

1. Select and move one atom at random and calculate the new distribution function, structure factor and:

$\chi _{n}^{2}=\sum _{i}(S_{n}^{C}(Q_{i})-S^{E}(Q_{i}))^{2}/\sigma (Q_{i})^{2}$

---

References

1. R.L.McGreevy and L. Pusztai, Mol. Simulation, 1 359-367 (1988)