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Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids

Authors :
Liu, Ke
Sadoune, Nicolas
Rao, Nihal
Greitemann, Jonas
Pollet, Lode
Source :
Phys. Rev. Research 3, 023016 (2021)
Publication Year :
2020

Abstract

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-$\Gamma$ 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 $\Gamma$ 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.<br />Comment: 17 pages, 12 figures, 2 tables; Editors' Suggestion

Details

Database :
arXiv
Journal :
Phys. Rev. Research 3, 023016 (2021)
Publication Type :
Report
Accession number :
edsarx.2004.14415
Document Type :
Working Paper
Full Text :
https://doi.org/10.1103/PhysRevResearch.3.023016