Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures

TLDR

We present a new Reverse Monte Carlo technique that generates 3‑D particle configurations consistent with experimentally measured structure factors using Markov chain Monte Carlo sampling. The method employs standard Monte Carlo with Markov chain sampling to produce 3‑D configurations, evaluates the structure factor A(Q) and radial distribution function g(r), and accepts configurations via a χ² test against experimental errors. The technique requires no input potential, yields initial results for liquid argon, and is promising for modeling glasses, amorphous materials, multicomponent systems, and experimental data analysis.

Abstract

Abstract We have developed a new technique, based on the standard Monte Carlo simulation method with Markov chain sampling, in which a set of three dimensional particle configurations are generated that are consistent with the experimentally measured structure factor. A(Q), and radial distribution function, g(r), of a liquid or other disordered system. Consistency is determined by a standard χ2 test using the experimental errors. No input potential is required, we present initial results for liquid argon. Since the technique can work directly from the structure factor it promises to be useful for modelling the structures of glasses or amorphous materials. It also has other advantages in multicomponent systems and as a tool for experimental data analysis.