K -means clustering was carried out to identify the atomic structure of nanocrystalline aluminum. For this purpose, per-atom physical quantities were calculated by means of molecular dynamics simulations, such as the potential energy, stress components, and atomic volume. Statistical analysis revealed that potential energy, atomic volume and von Mises stress were relevant parameters to distinguish between fcc atoms and grain boundary atoms. These three parameters were employed with the K -means algorithm to establish two clusters, one corresponding to fcc atoms and another to GB atoms. When comparing the K -means classification performance with that of CNA, an F-1 score of 0.969 and a Matthews correlation coefficient of 0.859 were achieved. This approach differs from other traditional methods in that the quantities employed here do not require input settings such as the number of nearest neighbor nor a cut-off value. Therefore, K -means clustering could ...

中文翻译：

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基于K均值聚类的纳米晶铝的晶体结构和晶界识别。

进行K-均值聚类以鉴定纳米晶铝的原子结构。为此，通过分子动力学模拟来计算每个原子的物理量，例如势能，应力分量和原子量。统计分析表明，势能，原子量和冯·米塞斯应力是区分fcc原子和晶界原子的相关参数。这三个参数与K均值算法一起使用，建立了两个簇，一个簇对应于fcc原子，另一个簇对应于GB原子。将K均值分类性能与CNA进行比较时，F-1得分为0.969，马修斯相关系数为0.859。此方法与其他传统方法的不同之处在于，此处使用的数量不需要输入设置，例如最近邻居的数量或截止值。因此，K-均值聚类可以...