Servis Michael J, Clark Aurora E
Department of Chemistry, Washington State University, Pullman, Washington 99164, United States.
Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, Washington 99164, United States.
J Phys Chem A. 2021 May 13;125(18):3986-3993. doi: 10.1021/acs.jpca.0c11320. Epub 2021 Apr 30.
Structural heterogeneity is commonly manifested in solutions and liquids that feature competition of different interparticle forces. Identifying and characterizing heterogeneity across different length scales requires multimodal experimental measurement and/or the application of new techniques for the interrogation of atomistic simulation data. Within the latter, the parsing of networks of interparticle interactions (chemical networks) has been demonstrated to be a valuable tool for identifying subensembles of chemical environments. However, chemical networks can adopt a wide variety of topologies that challenge generalizable methods for identifying heterogeneous behavior, and few network analysis algorithms have been proposed for multiscale resolution. In this study, we apply a method of partitioning using the graph theoretic concept of clusters and communities. Using a modularity optimization algorithm, the cluster partition creates subgraphs based on their relative internal and external connectivities. The methodology is tested on two soft matter systems that have significantly different network topologies so as to probe its ability to identify multiple scale features and its generalizability. A binary Lennard-Jones fluid is first examined, where one component causes subgraphs that have high internal network connectivity yet are still connected to the rest of the interparticle network of interactions. The impact of connectivity and edge weighting on the cluster partition is investigated. In the second system, hierarchically organized molecular structures comprised of hydrogen bonded water molecules are identified at a liquid/liquid interface. These structures have a much more sparse network with significantly varied internal connectivity that is a challenge to differentiate from the background hydrogen bonding network of water molecules at the instantaneous interface. The organized macrostructures are effectively isolated from the background network using the cluster partition, and a time-dependent implementation allows us to reveal their reactivity. These studies indicate that cluster partitioning based upon intermolecular network connectivity patterns is broadly generalizable, depending only on user-defined intermolecular connectivity, is operable across different length scales, and is extensible to the study of dynamic phenomena.
结构异质性通常表现在具有不同粒子间力竞争特征的溶液和液体中。识别和表征不同长度尺度上的异质性需要多模态实验测量和/或应用新技术来探究原子模拟数据。在后者中，粒子间相互作用网络（化学网络）的解析已被证明是识别化学环境子集合的有价值工具。然而，化学网络可以采用多种拓扑结构，这对识别异质行为的通用方法提出了挑战，并且很少有网络分析算法被提出用于多尺度分辨率。在本研究中，我们应用一种基于聚类和社区的图论概念进行划分的方法。使用模块化优化算法，聚类划分根据子图的相对内部和外部连通性创建子图。该方法在两个具有显著不同网络拓扑结构的软物质系统上进行了测试，以探究其识别多尺度特征的能力及其通用性。首先研究了二元 Lennard-Jones 流体，其中一种组分导致子图具有高内部网络连通性，但仍与粒子间相互作用网络的其余部分相连。研究了连通性和边权重对聚类划分的影响。在第二个系统中，在液/液界面处识别出由氢键水分子组成的分层组织的分子结构。这些结构具有更稀疏的网络，内部连通性差异很大，这对与瞬时界面处水分子的背景氢键网络区分开来构成了挑战。使用聚类划分有效地将有组织的宏观结构与背景网络隔离开来，并且时间相关的实现使我们能够揭示它们的反应性。这些研究表明，基于分子间网络连通性模式的聚类划分具有广泛的通用性，仅取决于用户定义的分子间连通性，可在不同长度尺度上操作，并且可扩展到动态现象的研究。
