Refrustration and competing orders in the prototypical Dy2Ti2O7 spin ice material

Spin ices, frustratedmagnetic materials analogous to common water ice, have emerged over the past 15 years as exemplars of high frustration in three dimensions. Recent experimental developments aimed at interrogating anew the low-temperature properties of these systems, in particular whether the predicted transition to long-range order occurs, behoove researchers to scrutinize our current dipolar spin ice model description of these materials. In this work, we do so by combining extensive Monte Carlo simulations and mean-field theory calculations to analyze data from previous magnetization, diffuse neutron scattering, and specific-heat measurements on the paradigmatic Dy2Ti2O7 spin ice material. In this work, we also reconsider the possible importance of the nuclear specific heat C-nuc in Dy2Ti2O7. We find that C-nuc is not entirely negligible below a temperature similar to 0.5 K and must therefore be taken into account in a quantitative analysis of the calorimetric data of this compound below that temperature. We find that in this material, small effective spin-spin exchange interactions compete with the magnetostatic dipolar interaction responsible for the main spin ice phenomenology. This causes an unexpected " refrustration" of the long-range order that would be expected from the incompletely self-screened dipolar interaction and which positions the material at the boundary between two competing classical long-range-ordered ground states. This allows for the manifestation of new physical low-temperature phenomena in Dy2Ti2O7, as exposed by recent specific-heat measurements. We show that among the four most likely causes for the observed upturn of the specific heat at low temperature [an exchange-induced transition to long-range order, quantum non-Ising (transverse) terms in the effective spin Hamiltonian, the nuclear hyperfine contribution, and random disorder], only the last appears to be reasonably able to explain the calorimetric data.