Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, Montpellier, France.
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.
J Chem Phys. 2020 Apr 14;152(14):144502. doi: 10.1063/5.0004732.
We present an information-theoretic approach inspired by distributional clustering to assess the structural heterogeneity of particulate systems. Our method identifies communities of particles that share a similar local structure by harvesting the information hidden in the spatial variation of two- or three-body static correlations. This corresponds to an unsupervised machine learning approach that infers communities solely from the particle positions and their species. We apply this method to three models of supercooled liquids and find that it detects subtle forms of local order, as demonstrated by a comparison with the statistics of Voronoi cells. Finally, we analyze the time-dependent correlation between structural communities and particle mobility and show that our method captures relevant information about glassy dynamics.
我们提出了一种基于分布聚类的信息论方法，用于评估颗粒系统的结构异质性。我们的方法通过挖掘两体或三体静态相关函数空间变化中隐藏的信息，识别具有相似局部结构的颗粒群。这对应于一种仅从颗粒位置及其种类推断群集的无监督机器学习方法。我们将这种方法应用于三种过冷液体模型，发现它可以检测到局部有序的微妙形式，这可以通过与 Voronoi 细胞统计数据的比较来证明。最后，我们分析了结构群集与颗粒迁移率之间的时变相关性，并表明我们的方法可以捕捉到有关玻璃动力学的相关信息。
