Machine-learned phase diagrams of generalized Kitaev honeycomb magnets

Rao, Nihal; Liu, Ke; Machaczek, Marc; Pollet, Lode

doi:10.1103/physrevresearch.3.033223

Cited by 20 publications

(11 citation statements)

References 79 publications

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“…Two-dimensional layered compounds have the strongest quantum spin fluctuations due to lower coordination numbers, and there is competition among magnetic interactions, giving them significant magnetic anisotropy. , As typical two-dimensional spin layer compounds, honeycomb lattice compounds have attracted much attention due to their special spatial structure. , When only the nearest magnetic interaction exists, it has no magnetic frustration, and it shows frustration when an extra competing exchange is considered, unlike triangular or Kagome lattice compounds. − In the special honeycomb structure, the magnetic structure is regulated by both honeycomb layers formed by magnetic ions and the intermediate layers formed by nonmagnetic ions. The special regulation makes it a three-dimensional magnetic interaction.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field

Xiao

Niu

et al. 2023

J. Phys. Chem. C

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The honeycomb single-crystal compound Na2Ni2TeO6 has been synthesized successfully, and its magnetization behavior under a pulsed high magnetic field has been investigated. At T N ∼ 27 K, Na2Ni2TeO6 undergoes a paramagnetic (PM) to antiferromagnetic (AFM) phase transition with both the magnetic field parallel to the c-axis (B∥c) and the magnetic field perpendicular to the c-axis (B⊥c). For B∥c and T = 2 K, a field-induced spin-reorientation transition is observed at B ∥ c1 ∼ 10.95 T and reached the canted AFM phase; as the magnetic field increases to B ∥ c2 ∼ 49.5 T, the spin is almost completely polarized and the PM state reached. For B⊥c and T = 4.2 K, a spin-reorientation transition was observed at B ⊥ c ∼ 53.5 T. Moreover, with the increase of temperature, the transition critical field moves to lower field regions, and with the increase of magnetic field, the critical temperature moves to the lower region. The Na2Ni2TeO6 single crystal shows obvious magnetic anisotropy, and the magnetic anisotropy does not disappear until the magnetic field is up to 56 T. Our experimental results confirm the uniaxial magnetocrystalline anisotropy related to the Ising-like spin configuration. Based on the temperature dependence of magnetization measurements, a complete magnetic phase diagram of Na2Ni2TeO6 was drawn.

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Section: Introductionmentioning

confidence: 99%

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field

Xiao

Niu

et al. 2023

J. Phys. Chem. C

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“…For example, Ref. [52] showed that, within a classical Monte Carlo sampling scheme, a whole zoo of intermediate phases appears between the low-field zigzag phase and the high-field po-larized state [53][54][55]. Classically, these are characterized as non-collinear/coplanar states with large magnetic unit cells and their weak long-range order is expected to be unstable upon the inclusion of quantum and/or thermal fluctuations [56].…”

Section: Introductionmentioning

confidence: 99%

Thermal spin dynamics of Kitaev magnets -- scattering continua and magnetic field induced phases within a stochastic semiclassical approach

Franke¹,

Călugăru²,

Nunnenkamp³

et al. 2022

Preprint

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The honeycomb magnet α−RuCl3 is a prime candidate material for realizing the Kitaev quantum spin liquid (QSL), but it shows long-range magnetic order at low temperature. Nevertheless, its broad inelastic neutron scattering (INS) response at finite frequency has been interpreted as that of a 'proximate QSL'. A moderate in-plane magnetic field indeed melts the residual zigzag order, giving rise to peculiar intermediate field phases before the high-field polarized state. In INS measurements the low-frequency spin waves disappear, leading to a broad scattering continuum in the field-induced intermediate regime, whose nature is currently under debate. Here, we study the magnetic field dependent spin dynamics of the K − Γ − Γ −model within a stochastic semiclassical treatment, which incorporates the effect of finite-temperature fluctuations. At temperatures relevant for INS experiments, we show how the excitations of the zigzag phase broaden and that the different intermediate phases all show a similar continuum response. We discuss the implications of our results for experiments and highlight the importance of distinguishing finite temperature fluctuations from genuine quantum fractionalization signatures in frustrated magnets.

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“…For example, autoencoders have been used to extract models from neutron scattering data in spin ice systems 6,7 . Support vector machines with a tensorial kernel have been used to explore tensor order parameters and hidden order in non-trivial frustrated models such as the XXZ model on the pyrochlore lattice 8 , the classical kagome antiferromagnet 9 , and Kitaev models and materials [10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques to construct detailed phase diagrams for skyrmion systems

Albarracín,

Rosales

2022

Preprint

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Recently, there has been an increased interest in the application of machine learning (ML) techniques to a variety of problems in condensed matter physics. In this regard, of particular significance is the characterization of simple and complex phases of matter. Here, we use a ML approach to construct the full phase diagram of a well known spin model combining ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions where topological phases emerge. At low temperatures, the system is tuned from a spiral phase to a skyrmion crystal by a magnetic field. However, thermal fluctuations induce two types of intermediate phases, bimerons and skyrmion gas, which are not as easily determined as spirals or skyrmion crystals. We resort to large scale Monte Carlo simulations to obtain low temperature spin configurations, and train a convolutional neural network (CNN), taking only snapshots at specific values of the DM couplings, to classify between the different phases, focusing on the intermediate and intricate topological textures. We then apply the CNN to higher temperature configurations and to other DM values, to construct a detailed magnetic field-temperature phase diagram, achieving outstanding results. We discuss the importance of including the disordered paramagnetic phases in order to get the phase boundaries, and finally, we compare our approach with other ML algorithms.

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Machine-learned phase diagrams of generalized Kitaev honeycomb magnets

Cited by 20 publications

References 79 publications

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field

Thermal spin dynamics of Kitaev magnets -- scattering continua and magnetic field induced phases within a stochastic semiclassical approach

Machine learning techniques to construct detailed phase diagrams for skyrmion systems

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Machine-learned phase diagrams of generalized Kitaev honeycomb magnets

Cited by 20 publications

References 79 publications

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na2Ni2TeO6 Single Crystal under a High Magnetic Field

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na2Ni2TeO6 Single Crystal under a High Magnetic Field

Thermal spin dynamics of Kitaev magnets -- scattering continua and magnetic field induced phases within a stochastic semiclassical approach

Machine learning techniques to construct detailed phase diagrams for skyrmion systems

Contact Info

Product

Resources

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Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field

Magnetic Properties and Phase Diagram of the Honeycomb Layered Na₂Ni₂TeO₆ Single Crystal under a High Magnetic Field