论文标题
一种一般分类方案，用于检测超冷液体中的空间和动态异质性
A general classification scheme of detecting spatial and dynamical heterogeneities in super-cooled liquids
论文作者
论文摘要
通过实施原理成分分析（PCA）和高斯混合物（GM）聚类方法（ML）算法来识别超冷液体的域结构的计算方法。原始特征数据是从使用其径向分布函数平滑的颗粒的配位数中收集的，并用作降低PCA维度降低后GM聚类的无序结构的订单参数。为了将知识从特征（结构）空间转移到配置空间，使用笛卡尔坐标作为订单参数进行另一种GM聚类，其中粒子在特征空间中的粒子身份。两种GM聚类均经过迭代进行，直到收敛为止。结果表明，在具有异质动力学的结构和构型空间中，纳米域的聚集簇的出现在足够长的时间尺度上。更重要的是，无论观察到系统大小如何，都可以将一致的纳米域耕种耕作，并且我们的方法可以应用于任何无序系统。
A computational approach via implementation of the Principle Component Analysis (PCA) and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify domain structures of supercooled liquids is developed. Raw features data are collected from the coordination numbers of particles smoothed using its radial distribution function and are used as an order-parameter of disordered structures for GM clustering after dimensionality reduction from the PCA. To transfer the knowledge from features(structural) space to configurational space, another GM clustering is performed using the Cartesian coordinates as an order-parameter with the particles' identity from GM in the feature space. Both GM clustering are performed iteratively until convergence. Results show the appearance of aggregated clusters of nano-domains over sufficient long timescale both in structural and configurational spaces with heterogeneous dynamics. More importantly, consistent nano-domains tilling up the whole space regardless of the system size are observed and our approach can be applied to any disordered systems.
