Neural-network-based order parameters for classification of binary hard-sphere crystal structures
Neural-network-based order parameters for classification of binary hard-sphere crystal structures
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DOI：
10.1080/00268976.2018.1483537
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发表时间：
2018-01-01
影响因子：
1.7
通讯作者：
Filion, Laura
中科院分区：
文献类型：
--
作者：
Boattini, Emanuele;Ram, Michel;Filion, Laura
在许多类型的研究中，确定晶体结构是一个共同的挑战。在这里，我们专注于不同尺寸比的硬球的二元混合物，它稳定了一系列具有不同复杂性的晶体结构。我们通过分析由几个平均局部键序参数组成的向量，训练前馈神经网络在单粒子基础上区分不同的晶体和流体环境。对于所有考虑的尺寸比，我们对所有相都达到了以上的分类精度，这意味着我们的方法是完全通用的，并且能够捕获大范围的二元晶体的结构差异。
Identifying crystalline structures is a common challenge in many types of research. Here, we focus on binary mixtures of hard spheres of various size ratios, which stabilise a range of crystal structures with varying complexity. We train feed-forward neural networks to distinguish different crystalline and fluid environments on a single-particle basis, by analysing vectors composed of several averaged local bond order parameters. For all size ratios considered, we achieve a classification accuracy above for all phases, meaning that our method is completely general and able to capture structural differences of a wide range of binary crystals.[GRAPHICS].