论文标题
通过社区推断评估超冷液体的结构异质性
Assessing the structural heterogeneity of supercooled liquids through community inference
论文作者
论文摘要
我们提出了一种受分布聚类启发的信息理论方法，以评估颗粒系统的结构异质性。我们的方法通过收集隐藏在两体或三体静态相关性的空间变化中的信息来确定颗粒的群落，这些粒子社区共享了相似的局部结构。这对应于一种无监督的机器学习方法，该方法仅从粒子位置及其物种中吸收社区。我们将此方法应用于三种模型的超冷液体模型，发现它检测到局部秩序的微妙形式，如与Voronoi细胞统计的比较所证明的那样。最后，我们分析了结构群落与粒子迁移率之间的时间依赖性相关性，并表明我们的方法捕获了有关玻璃动力学的相关信息。
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.
