arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.14415cond-mat.str-elcond-mat.mtrl-scics.LGphysics.comp-ph
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机器学习揭示Kitaev材料的Phase Diagram:自旋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

University of Munich(慕尼黑大学);Munich Center for Quantum Science and Technology(慕尼黑量子科学与技术中心);Shanghai Jiao Tong University(上海交通大学)

Ke Liu, Nicolas Sadoune, Nihal Rao, Jonas Greitemann, Lode Pollet

摘要:

Kitaev材料是研究量子自旋液体以及拓扑相和破缺相相互作用的理想材料。我们使用一个无监督和可解释的机器学习方法,张量核支持向量机,研究蜂窝Kitaev- $Γ$模型在磁场。我们的机器学习全局经典相图和相关的分析序参量,包括几种不同的自旋液体,两种奇异的$S_3$磁体和两种调制的$S_3 \times Z_3$磁体。我们发现,Kitaev自旋液体的扩展和磁场诱导的磁序抑制已经发生在大- $S$极限,这意味着Kitaev材料的物理学的关键部分可以在经典水平上理解。此外,两个$S_3 \times Z_3$订单诱导Kitaev和$Γ$自旋液体之间的竞争和功能不同类型的自旋晶格纠缠调制,这需要一个矩阵描述,而不是标量相位因子。我们的工作提供了一个机器检测新相位的直接实例,并为开发自动化工具探索多体物理学中未解决的问题铺平了道路。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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