Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Phys Rev E. 2019 Feb;99(2-1):022903. doi: 10.1103/PhysRevE.99.022903.
Structural defects within amorphous packings of symmetric particles can be characterized using a machine learning approach that incorporates structure functions of radial distances and angular arrangement. This yields a scalar field, softness, that correlates with the probability that a particle is about to be rearranged. However, when particle shapes are elongated, as in the case of dimers and ellipses, we find that the standard structure functions produce imprecise softness measurements. Moreover, ellipses exhibit deformation profiles in stark contrast to circular particles. In order to account for the effects of orientation and alignment, we introduce structure functions to recover the predictive performance of softness, as well as provide physical insight into local and extended dynamics. We study a model disordered solid, a bidisperse two-dimensional granular pillar, driven by uniaxial compression and composed entirely of monomers, dimers, or ellipses. We demonstrate how the computation of softness via a support vector machine extends to dimers and ellipses with the introduction of orientational structure functions. Then we highlight the spatial extent of rearrangements and defects, as well as their cross correlation, for each particle shape. Finally, we demonstrate how an additional machine learning algorithm, recursive feature elimination, provides an avenue to better understand how softness arises from particular structural aspects. We identify the most crucial structure functions in determining softness and discuss their physical implications.
采用机器学习方法，可以对具有对称性的无定形颗粒的结构缺陷进行分析，该方法涉及到径向距离和角分布的结构函数。这会产生一个标量场，即柔软度，它与粒子即将发生重排的概率相关。然而，当粒子形状呈长形时，如在二聚体和椭圆体的情况下，我们发现标准结构函数会产生不精确的柔软度测量结果。此外，椭圆体表现出的变形特征与圆形粒子形成鲜明对比。为了考虑取向和对齐的影响，我们引入结构函数来恢复柔软度的预测性能，并提供对局部和扩展动力学的物理洞察力。我们研究了一种模型无序固体，即由单个体、二聚体或椭圆体组成的双分散二维颗粒柱，由单轴压缩驱动。我们展示了如何通过支持向量机计算柔软度，以及如何通过引入取向结构函数将其扩展到二聚体和椭圆体。然后，我们突出显示了每种粒子形状的重排和缺陷的空间范围及其交叉相关性。最后，我们展示了如何使用另一种机器学习算法，递归特征消除，来更好地理解柔软度如何由特定的结构方面产生。我们确定了确定柔软度的最关键的结构函数，并讨论了它们的物理意义。
