论文标题
从检测明确的顺序参数中推断出外来磁体的隐藏对称性
Inferring Hidden Symmetries of Exotic Magnets from Detecting Explicit Order Parameters
论文作者
论文摘要
可以通过存在高对称点的存在将非常规的磁铁映射到简单的铁磁铁上。然后，可以将传统铁磁系统的知识传递，以提供对更复杂秩序的洞察力。在这里，我们证明了如何使用无监督和可解释的机器学习方法来搜索非常规磁铁中潜在的高对称点，而无需任何先行的系统知识。该方法应用于蜂窝晶格上的经典Heisenberg-Kitaev模型，在那里我们的机器学习了显示其隐藏的$ O（3）$对称性的转换，而无需使用这些高对称点的数据。此外，我们澄清说，与条纹和锯齿形订单相反，一组$ d_2 $和$ d_ {2h} $订购矩阵提供了对海森伯格 - 基塔夫模型中磁化的更完整描述。此外，我们的机器还学习了相位边界处的局部约束，这表现出了差异对称性。本文强调了明确的订单参数对多体旋转系统的重要性以及可解释性的属性，用于机器学习技术的物理应用。
An unconventional magnet may be mapped onto a simple ferromagnet by the existence of a high-symmetry point. Knowledge of conventional ferromagnetic systems may then be carried over to provide insight into more complex orders. Here we demonstrate how an unsupervised and interpretable machine-learning approach can be used to search for potential high-symmetry points in unconventional magnets without any prior knowledge of the system. The method is applied to the classical Heisenberg-Kitaev model on a honeycomb lattice, where our machine learns the transformations that manifest its hidden $O(3)$ symmetry, without using data of these high-symmetry points. Moreover, we clarify that, in contrast to the stripy and zigzag orders, a set of $D_2$ and $D_{2h}$ ordering matrices provides a more complete description of the magnetization in the Heisenberg-Kitaev model. In addition, our machine also learns the local constraints at the phase boundaries, which manifest a subdimensional symmetry. This paper highlights the importance of explicit order parameters to many-body spin systems and the property of interpretability for the physical application of machine-learning techniques.
