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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.
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 inelas
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
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
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. S
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