2022/07/03 by L. Aramis de Icaza Astiz, Astiz, L. Aramis de Icaza, Atahualpa S. Kraemer +3
Engineering · Materials Science · Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Artificial intelligence #Binary number #Chaotic Dynamics (nlin.CD) #Combinatorics #Computational Physics (physics.comp-ph) #Computer science #Distribution (mathematics) #Entropy (arrow of time) #FOS: Physical sciences #Interval (graph theory) #Material Dynamics and Properties #Mathematical analysis #Mathematics #Order (exchange) #Phase Equilibria and Thermodynamics #Phase transition #Physics #Principle of maximum entropy #Statistical Mechanics (cond-mat.stat-mech) #Statistical physics #Thermodynamics
paper · pdf · doi:10.48550/arxiv.2207.01013
openalex publication_date 2022/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.