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Register a new userMelting of Spin Ice state through structural disorder in Dy2Zr2O7
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Neutron scattering, a.c. magnetic susceptibility and specific heat studies have been carried out on polycrystalline Dy2Zr2O7. Unlike the pyrochlore spin ice Dy2Ti2O7, Dy2Zr2O7 crystallizes into the fluorite structure and the magnetic Dy3+ moments randomly reside on the corner-sharing tetrahedral sublattice with non-magnetic Zr ions. Antiferromagnetic spin correlations develop below 10 K but remain dynamic down to 40 mK. These correlations extend over the length of two tetrahedra edges and grow to 6 nearest neighbors with the application of a 20 kOe magnetic field. No Paulings residual entropy was observed and by 8 K the full entropy expected for a two level system is released. We propose that the disorder melts the spin ice state seen in the chemically ordered Dy2Ti2O7 compound, but the spins remain dynamic in a disordered, liquid-like state and do not freeze into a glass-like state that one might intuitively expect.
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In spin ice research, small variations in structure or interactions drive a multitude of different behaviors, yet the collection of known materials relies heavily on the `227 pyrochlore structure. Here, we present thermodynamic, structural and inelastic neutron scattering data on a new spin-ice material, MgEr$_2$Se$_4$, which contributes to the relatively under-explored family of rare-earth spinel chalcogenides. X-ray and neutron diffraction confirm a normal spinel structure, and places Er$^{3+}$ moments on an ideal pyrochlore sublattice. Measurement of crystal electric field excitations with inelastic neutron scattering confirms that the moments have perfect Ising character, and further identifies the ground state Kramers doublet as having dipolar-octupolar form with a significant multipolar character. Heat capacity and magnetic neutron diffuse scattering have ice-like features, but are inconsistent with Monte Carlo simulations of the nearest-neighbor and next-nearest-neighbor dipolar spin-ice (DSI) models. A significant remnant entropy is observed as $Trightarrow0$~K, but again falls short of the full Pauling expectation for DSI, unless significant disorder is added. We show that these observations are fully in-line with what is recently reported for CdEr$_2$Se$_4$, and point to the importance of quantum fluctuations in these materials.
Neutron scattering experiments on a polycrystalline sample of the frustrated pyrochlore magnet Tb2Ti2O7, which does not show any magnetic order down to 50 mK, have revealed that it shows condensation behavior below 0.4 K from a thermally fluctuating paramagnetic state to a spin-liquid ground-state with quantum spin fluctuations. Energy spectra change from quasielastic scattering to a continuum with a double-peak structure at energies of 0 and 0.8 K in the spin-liquid state. Specific heat shows an anomaly at the crossover temperature.
Dy2Ti2O7 has been advanced as an ideal spin ice. We present a neutron scattering investigation of a sample of 162Dy2Ti2O7. The scattering intensity has been mapped in zero applied field in the hhl and hk0 planes at temperatures between 0.05 K and 20 K. The measured diffuse scattering (in the static approximation) has been compared to that predicted by the dipolar spin ice model. The comparison is good, except at the Brillouin zone boundaries where extra scattering appears in the experimental data. It is concluded that the dipolar spin ice model provides a successful basis for understanding Dy2Ti2O7, but that there are issues which remain to be clarified.
Artificial ice systems have unique physical properties promising for potential applications. One of the most challenging issues in this field is to find novel ice systems that allows a precise control over the geometries and many-body interactions. Superconducting vortex matter has been proposed as a very suitable candidate to study artificial ice, mainly due to availability of tunable vortex-vortex interactions and the possibility to fabricate a variety of nanoscale pinning potential geometries. So far, a detailed imaging of the local configurations in a vortex-based artificial ice system is still lacking. Here we present a direct visualization of the vortex ice state in a nanostructured superconductor. By using the scanning Hall probe microscopy, a large area with the vortex ice ground state configuration has been detected, which confirms the recent theoretical predictions for this new ice system. Besides the defects analogous to artificial spin ice systems, other types of defects have been visualized and identified. We also demonstrate the possibility to realize different types of defects by varying the magnetic field.
We present a deep reinforcement learning framework where a machine agent is trained to search for a policy to generate a ground state for the square ice model by exploring the physical environment. After training, the agent is capable of proposing a sequence of local moves to achieve the goal. Analysis of the trained policy and the state value function indicates that the ice rule and loop-closing condition are learned without prior knowledge. We test the trained policy as a sampler in the Markov chain Monte Carlo and benchmark against the baseline loop algorithm. This framework can be generalized to other models with topological constraints where generation of constraint-preserving states is difficult.
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