论文标题
通过机器学习揭示基塔夫材料的相图：旋转液体之间的合作与竞争
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
论文作者
论文摘要
Kitaev材料是托管量子自旋液体并研究拓扑和对称阶段的相互作用的有希望的材料。我们使用一种无监督且可解释的机器学习方法，即张力 - 内核支持向量机，来研究磁场中的蜂窝基塔伊（Kitaev-kitaev）$γ$模型。我们的机器学习了全球经典相图和相关的分析顺序参数，包括几种不同的旋转液体，两个奇异的$ S_3 $磁铁和两个调制的$ S_3 \ Times Z_3 $磁铁。我们发现，Kitaev旋转液体的扩展和磁场引起的磁性抑制已经发生在巨大的限制中，这意味着可以在经典水平上理解Kitaev材料物理的关键部分。此外，两个$ S_3 \ times z_3 $订单是由Kitaev和$γ$旋转液体之间的竞争引起的，并具有不同类型的自旋晶格纠缠调制，这需要矩阵描述而不是标量相位因子。我们的工作提供了一个机器检测新阶段的直接实例，并为开发自动化工具的开发铺平了探索多体物理中未解决问题的方式。
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases. We use an unsupervised and interpretable machine-learning method, the tensorial-kernel support vector machine, to study the honeycomb Kitaev-$Γ$ model in a magnetic field. Our machine learns the global classical phase diagram and the associated analytical order parameters, including several distinct spin liquids, two exotic $S_3$ magnets, and two modulated $S_3 \times Z_3$ magnets. We find that the extension of Kitaev spin liquids and a field-induced suppression of magnetic order already occur in the large-$S$ limit, implying that critical parts of the physics of Kitaev materials can be understood at the classical level. Moreover, the two $S_3 \times Z_3$ orders are induced by competition between Kitaev and $Γ$ spin liquids and feature a different type of spin-lattice entangled modulation, which requires a matrix description instead of scalar phase factors. Our work provides a direct instance of a machine detecting new phases and paves the way towards the development of automated tools to explore unsolved problems in many-body physics.
