Walters Michael, Wei Qianshi, Chen Jeff Z Y
Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1.
Phys Rev E. 2019 Jun;99(6-1):062701. doi: 10.1103/PhysRevE.99.062701.
Supervised machine learning can be used to classify images with spatially correlated physical features. We demonstrate the concept by using the coordinate files generated from an off-lattice computer simulation of rodlike molecules confined in a square box as an example. Because of the geometric frustrations at high number density, the nematic director field develops an inhomogeneous pattern containing various topological defects as the main physical feature. We describe two machine-learning procedures that can be used to effectively capture the correlation between the defect positions and the nematic directors around them and hence classify the topological defects. First is a feedforward neural network, which requires the aid of presorting the off-lattice simulation data in a coarse-grained fashion. Second is a recurrent neural network, which needs no such sorting and can be directly used for finding spatial correlations. The issues of when to presort a simulation data file and how the network structures affect such a decision are addressed.
监督式机器学习可用于对具有空间相关物理特征的图像进行分类。我们以从方形盒子中受限的棒状分子的非晶格计算机模拟生成的坐标文件为例来说明这一概念。由于在高数量密度下的几何挫折，向列指向矢场会形成包含各种拓扑缺陷的不均匀图案，这是主要的物理特征。我们描述了两种机器学习程序，它们可用于有效捕捉缺陷位置与其周围向列指向矢之间的相关性，从而对拓扑缺陷进行分类。第一种是前馈神经网络，它需要以粗粒度方式对非晶格模拟数据进行预排序。第二种是循环神经网络，它不需要这样的排序，并且可以直接用于查找空间相关性。我们还讨论了何时对模拟数据文件进行预排序以及网络结构如何影响这一决策的问题。
