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Analysis of Diffuse Scattering from Single Crystals via the Reverse Monte Carlo Technique. I. Comparison with Direct Monte Carlo
Author(s) -
Welberry T. R.,
Proffen Th.
Publication year - 1998
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s0021889897010789
Subject(s) - reverse monte carlo , monte carlo method , statistical physics , range (aeronautics) , spurious relationship , diffraction , scattering , computational physics , physics , mathematics , optics , statistics , materials science , neutron diffraction , composite material
The use of the reverse Monte Carlo (RMC) technique for analysing diffuse scattering data from single crystals is compared with the well established direct Monte Carlo (MC) method. Whereas in the MC method a model involving only a few interatomic interaction parameters is used, for RMC the atom coordinates themselves are the variables and problems related to underdeterminacy can arise. Attempts to use the RMC technique to obtain short‐range correlation information for a relatively simple real physical system, the Tl cation distribution in TlSbOGeO 4 , are described. It is found that the RMC method has two conflicting requirements. If the size of the model system is sufficiently large to give a workably smooth calculated diffraction pattern, then the number of variables inherent in the structure is so large that it far exceeds the number of observed data, and the fit obtained is completely spurious. On the other hand, if the model system is kept sufficiently small so that the number of observations greatly exceeds the number of variables, then the calculated diffraction pattern is so noisy that meaningful short‐range correlation information is difficult to discern. Even for small systems, it appears that the RMC refinement using the goodness‐of‐fit parameter, χ 2 , results in adjustment of the many longer‐range correlations to obtain the fit rather than the relatively few short‐range correlations. Despite the poor performance of the currently implemented RMC algorithm for extracting short‐range correlation information, there are some grounds for optimism and the method can provide useful information. Although the derived short‐range correlation values present in the final refined coordinates were barely significantly different from zero, it was nevertheless possible to discern consistent trends as the simulations progressed that could provide useful guidance in establishing a better MC model. Ways in which the RMC methodology might be improved have been suggested by the study, although these would require even greater computational resources.