On the Characterization of Inhomogeneity of the Density Distribution in Supercritical Fluids via Molecular Dynamics Simulation and Data Mining Analysis

We combined molecular dynamics simulation and DBSCAN algorithm (Density Based Spatial Clustering of Application with Noise) in order to characterize the local density inhomogeneity distribution in supercritical fluids. The DBSCAN is an algorithm that is capable of finding arbitrarily shaped density domains, where domains are defined as dense regions separated by low-density regions. The inhomogeneity of density domain distributions of Ar system in sub- and supercritical conditions along the 50 bar isobar is associated with the occurrence of a maximum in the fluctuation of number of particles of the density domains. This maximum coincides with the temperature, T α, at which the thermal expansion occurs. Furthermore, using Voronoi polyhedral analysis, we characterized the structure of the density domains. The results show that with increasing temperature below T α, the increase of the inhomogeneity is mainly associated with the density fluctuation of the border particles of the density domains, while with increasing temperature above T α, the decrease of the inhomogeneity is associated with the core particles.