Abstract

A challenging problem in particle-based modeling is one of classifying the many structures which involve very large networks of bonds. Based on capacity to judge if a system is amorphous or solid from radial distribution functions, we set up two machine-learning systems able to identify local structures in mono-component hard-sphere simulations. The machines are constituted of logistic or support-vector regressions applied to linear model, second- and third-degree polynomial hypothesis. We labeled the sphere as solid or amorphous following a bond-order parameter and characterized them with radial structure functions. The features were enough to machine-learning systems predicting the labels with great accuracy.

Graphic abstract

中文翻译：

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使用机器学习确定硬球系统中的邻域相位

摘要

基于粒子的建模中的一个具有挑战性的问题是对涉及非常大的键网络的许多结构进行分类。基于从径向分布函数判断系统是无定形还是固体的能力，我们建立了两个机器学习系统，能够在单分量硬球模拟中识别局部结构。这些机器由应用于线性模型、二阶和三阶多项式假设的逻辑回归或支持向量回归组成。我们根据键序参数将球体标记为固体或非晶态，并用径向结构函数对其进行表征。这些特征足以让机器学习系统非常准确地预测标签。

图形摘要