title: Geometrical ambiguity of pair statistics: point configurations.
abstract: Point configurations have been widely used as model systems in condensed-matter physics, materials science, and biology. Statistical descriptors, such as the n -body distribution function g(n), are usually employed to characterize point configurations, among which the most extensively used is the pair distribution function g(2). An intriguing inverse problem of practical importance that has been receiving considerable attention is the degree to which a point configuration can be reconstructed from the pair distribution function of a target configuration. Although it is known that the pair-distance information contained in g(2) is, in general, insufficient to uniquely determine a point configuration, this concept does not seem to be widely appreciated and general claims of uniqueness of the reconstructions using pair information have been made based on numerical studies. In this paper, we present the idea of the distance space called the D space. The pair distances of a specific point configuration are then represented by a single point in the D space. We derive the conditions on the pair distances that can be associated with a point configuration, which are equivalent to the realizability conditions of the pair distribution function g(2). Moreover, we derive the conditions on the pair distances that can be assembled into distinct configurations, i.e., with structural degeneracy. These conditions define a bounded region in the D space. By explicitly constructing a variety of degenerate point configurations using the D space, we show that pair information is indeed insufficient to uniquely determine the configuration in general. We also discuss several important problems in statistical physics based on the D space, including the reconstruction of atomic structures from experimentally obtained g(2) and a recently proposed "decorrelation" principle. The degenerate configurations have relevance to open questions involving the famous traveling salesman problem.
authors.affiliation: Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Jiao
authors.forename: F H
authors.initials: FH
authors.lastname: Stillinger
authors.forename: S
authors.initials: S
authors.lastname: Torquato
mesh_terms: D000465:Algorithms; D015233:Models, Statistical