Generalized molecular distribution functions
Arieh Y. Ben-Naim
Abstract
Arieh Y. Ben-Naim
Abstract
Molecular distribution functions are concerned with the probability densities of observing a set of n particles at some specified locations. The generalized molecular distribution functions ``load'' the n particles with additional information, which may include or exclude their locations. It is argued that the study of these functions may provide a new insight into the local properties of the molecules in the liquid state. A sample of computed generalized molecular distribution functions by the Monte Carlo technique is presented. The singlet and the pair distribution functions for coordination number (CN) and binding energy (BE) were computed for simple, spherical particles in two dimensions. Further examples of the singlet, distribution functions for nonspherical, waterlike particles are also reported, and some topics relevant to the mixture model approach to the theory of liquid water are discussed.
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Molecular distribution functions are concerned with the probability densities of observing a set of n particles at some specified locations. The generalized molecular distribution functions ``load'' the n particles with additional information, which may include or exclude their locations. It is argued that the study of these functions may provide a new insight into the local properties of the molecules in the liquid state. A sample of computed generalized molecular distribution functions by the Monte Carlo technique is presented. The singlet and the pair distribution functions for coordination number (CN) and binding energy (BE) were computed for simple, spherical particles in two dimensions. Further examples of the singlet, distribution functions for nonspherical, waterlike particles are also reported, and some topics relevant to the mixture model approach to the theory of liquid water are discussed.
Key concepts: Statistical physics, Singlet state, Distribution (mathematics), Pair distribution function, Monte Carlo method, Distribution function, Set (abstract data type), Simple (philosophy)