Non-monotonic residual entropy in diluted spin ice: a comparison between Monte Carlo simulations of diluted dipolar spin ice models and experimental results
T. Lin, X. Ke, M. Thesberg, P. Schiffer, R. G. Melko, M. J. P. Gingras

TL;DR
This study uses Monte Carlo simulations of diluted dipolar spin ice models to accurately reproduce experimental specific heat and residual entropy data in diluted spin ice materials, revealing non-monotonic entropy behavior with dilution.
Contribution
It provides a quantitative comparison between Monte Carlo simulations and experimental results, explaining non-monotonic residual entropy in diluted spin ice and highlighting the limits of current models at high dilution levels.
Findings
Simulations match experimental data up to 85% dilution.
Residual entropy varies non-monotonically with dilution.
Discrepancies appear at 90-95% dilution, indicating model limitations.
Abstract
Spin ice materials, such as Dy2Ti2O7 and Ho2Ti2O7, have been the subject of much interest for over the past fifteen years. Their low temperature strongly correlated state can be mapped onto the proton disordered state of common water ice and, consequently, spin ices display the same low temperature residual Pauling entropy as water ice. Interestingly, it was found in a previous study [X. Ke {\it et. al.} Phys. Rev. Lett. {\bf 99}, 137203 (2007)] that, upon dilution of the magnetic rare-earth ions (Dy^{3+} and Ho^{3+}) by non-magnetic Yttrium (Y^{3+}) ions, the residual entropy depends {\it non-monotonically} on the concentration of Y^{3+} ions. In the present work, we report results from Monte Carlo simulations of site-diluted microscopic dipolar spin ice models (DSIM) that account quantitatively for the experimental specific heat measurements, and thus also for the residual entropy, as…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
