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arXiv 2304.03469cond-mat.stat-mech
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通过机器学习实现过冷液体空间与动力学异质性的自动表征

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of Machine Learning

Viet Nguyen, Xueyu Song

AI总结:

本研究利用PCA、K-means和混合高斯聚类等机器学习方法,自动表征过冷液体的结构及动力学异质性,并揭示其源于淬火后的液-液相分离。

摘要:

本文开发了一种计算方法,通过实现机器学习算法中的主成分分析(PCA)与K-means和混合高斯(GM)聚类方法,来识别过冷液体的结构与动力学异质性。在该方法中,由粒子位置计算得到的粒子平均加权配位数($\overline{WCNs}$)集合被用作序参量,以构建特征(结构)空间的低维表示,用于K-means聚类,并借助PCA将系统中的粒子分为若干介观态。通过使用关联介观态的粒子身份进行直接映射,在构型(真实)空间中也形成了纳米域或聚集簇,其中包含一些被错误分类的界面粒子。这些分类不确定性可以通过一种共学习策略来改善,该策略利用概率性GM聚类以及结构空间与构型空间之间的信息传递,迭代直至收敛。结构空间中介观态和构型空间中域的最终分类在长时间内保持稳定,并被测量到具有动力学异质性。借助这样的分类协议,对这些域的热力学和动力学性质进行的多项研究表明,观察到的异质性是在淬火至过冷状态后液-液相分离的结果。

英文摘要:

A computational approach by an implementation of the Principle Component Analysis (PCA) with K-means and Gaussian Mixture (GM) clustering methods from Machine Learning (ML) algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ($\overline{WCNs}$) of particles calculated from particles' positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K-means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states' particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid-liquid phase separation after quenching to a supercooled state.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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