Data versus constraints in reverse Monte Carlo modelling: a case study on molecular liquid CCl₄

Abstract

In reverse Monte Carlo modelling, experimental information (i.e. diffraction data) and a priori information (i.e. constraints introduced in the algorithm) are partly redundant. The extent of this redundancy for ('fixed neighbour') constraints determining the molecular geometry is studied systematically in the typical case of liquid CCl4. Results indicate that data with a very limited momentum transfer range are sufficient for deriving the intermolecular structure of such disordered systems when (intra)molecular geometry and intermolecular distances of closest approach are introduced by the appropriate algorithmic constraints.