Analyzing and Driving Cluster Formation in Atomistic Simulations.
Analyzing and Driving Cluster Formation in Atomistic Simulations.
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DOI：
10.1021/acs.jctc.6b01073
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发表时间：
2017-02
影响因子：
5.5
通讯作者：
G. A. Tribello;F. Giberti;G. Sosso;M. Salvalaglio;M. Parrinello
中科院分区：
文献类型：
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作者：
G. A. Tribello;F. Giberti;G. Sosso;M. Salvalaglio;M. Parrinello
本文介绍了一套用于识别由原子或分子组成的系统中所含相的计算工具。该方法植根于图论，结合了原子中心对称函数、邻接矩阵和聚类算法来识别系统成分属性可以被认为是一致的空间区域。我们展示了如何使用这种方法来定义集体变量，以及如何使用这些集体变量来增强成核事件的采样。然后，我们通过分析分子晶体尿素成核的模拟和半导体合金成核的模拟，展示了如何将这种方法用于分析晶体成核和生长的模拟。我们讨论的半导体合金的例子特别具有挑战性，因为形成了多个成核中心。然而，我们证明，我们的算法能够检测到所得到的多晶中的晶界。
In this paper a set of computational tools for identifying the phases contained in a system composed of atoms or molecules is introduced. The method is rooted in graph theory and combines atom centered symmetry functions, adjacency matrices, and clustering algorithms to identify regions of space where the properties of the system constituents can be considered uniform. We show how this method can be used to define collective variables and how these collective variables can be used to enhance the sampling of nucleation events. We then show how this method can be used to analyze simulations of crystal nucleation and growth by using it to analyze simulations of the nucleation of the molecular crystal urea and simulations of nucleation in a semiconducting alloy. The semiconducting alloy example we discuss is particular challenging as multiple nucleation centers are formed. We show, however, that our algorithm is able to detect the grain boundaries in the resulting polycrystal.