Worlitzer Vasco M, Ariel Gil, Lazar Emanuel A
Department of Mathematics, Bar Ilan University, Ramat Gan 5290002, Israel.
Phys Rev E. 2023 Dec;108(6-1):064115. doi: 10.1103/PhysRevE.108.064115.
The pair correlation function (PCF) has proven an effective tool for analyzing many physical systems due to its simplicity and its applicability for simulated and experimental data. However, as an averaged quantity, the PCF can fail to capture subtle structural differences in particle arrangements, even when those differences can have a major impact on system properties. Here, we use Voronoi topology to introduce a discrete version of the PCF that highlights local interparticle topological configurations. The advantages of the Voronoi PCF are demonstrated in several examples including crystalline, hyperuniform, and active systems showing clustering and giant number fluctuations.
对关联函数（PCF）因其简单性以及对模拟数据和实验数据的适用性，已被证明是分析许多物理系统的有效工具。然而，作为一个平均值，即使粒子排列中的细微结构差异可能对系统性质产生重大影响，PCF也可能无法捕捉到这些差异。在这里，我们使用Voronoi拓扑结构来引入PCF的离散版本，该版本突出了局部粒子间的拓扑构型。Voronoi PCF的优势在几个例子中得到了证明，包括晶体、超均匀和表现出聚类和巨大数量波动的活性系统。
