Dynamic neighbors: a proposal of a tool to characterize phase transitions

TL;DR

This paper introduces a new method to characterize phase transitions in hard particle systems by analyzing the distribution of dynamic neighbors, which correlates with key physical properties and enhances detection of phase changes.

Contribution

It proposes a novel tool based on dynamic neighbors to better understand phase transitions in molecular dynamics simulations of hard particles.

Findings

Distribution of dynamic neighbors correlates with diffusion, structure, and entropy.

Using dynamic neighbors sharpens phase transition signals.

Applicable to 2D and 3D systems of hard disks and spheres.

Abstract

For molecular dynamics simulations of hard particles, we define dynamic neighbors as the distinct particles that collide with a given reference one during a specific time interval. This definition allows us to determine the distribution of the number of dynamic neighbors, its average, and its standard deviation. We will show that regardless of the time window used to identify dynamic neighbors, their distribution is correlated with diffusion coefficients, structure, and configurational entropy. Thus, it is likely that the distribution of the number of dynamic neighbors may be employed as another tool to gain insights into the dynamic behavior of hard systems. We tested this approach on 2D and 3D systems consisting of monodisperse and binary mixtures of hard disks and spheres. Results show that implementing dynamic neighbors to define order parameters can sharpen the signals where transitions take place.