Particle Diffusion in Correlated Disordered Media near Transition Point

Abstract

Particle diffusion of ions is investigated in a super-ionic conductor Agl in the liquid and a states near the melting point by observing the non-Gaussian parameter, the mean square displacement, the distribution function of particle and the intermediate function of density auto-correlation. Anomalous properties of particle diffusion are confirmed in the super-ionic conductor Agl, on some intermediate time range of the order of 1ps-30ps. It is well-known that anomalous dynamical properties such as subdiffusive behav­ ior of the mean square displacement (MSD) and as a and f3 type decay of the density auto-correlation function exist on some intermediate time scale in super-cooled liq­ uids near the glass transition. l) These anomalous properties exhibit non-Gaussian processes. It has also been reported by the authors by means of molecular dynamical (MD) simulation that the time development of the distribution function P(x, t) of the particle displacement does not obey normal Gaussian process on a similar time scale in the liquid state near the melting temperature T m and in the a-phase of Agl in thermal equilibrium. 2 ) In this paper we try to reinvestigate the super-ionic conductor Agl numerically by the MD method in the light of the recent study of the slow dynamics mentioned above. We observe, in addition to the MSD and the non-Gaussian parameter (NGP), the self part of the density auto-correlation function Fs(k, t), which is the Fourier transformation of the distribution function P(x, t) of the diffusing particles in the position x. In the function Fs(k, t), which is also directly related to the dynamical structure factor S(k, w) by Fourier transformation of the time variable t, the a and (3 relaxation processes have been observed. 1 ) In addition, the numerically obtained distribution function P(x, t) also has complementary information about the anomalous dynamical properties. It is thus worth revisiting this anomalous diffusion process in Agl by examining both the function F8 (k, t) and the distribution function P(x, t).