Distinct spin-lattice and spin-phonon interactions in monolayer magnetic CrI$_3$
Lucas Webster, Liangbo Liang, Jia-An Yan

TL;DR
This study uses density-functional theory to explore how magnetic ordering affects the electronic, vibrational, and Raman properties of monolayer CrI$_3$, revealing distinct spin-lattice and spin-phonon interactions.
Contribution
It provides a detailed first-principles analysis of magnetic, electronic, and vibrational properties of monolayer CrI$_3$, highlighting the interplay between magnetism and lattice dynamics.
Findings
Magnetism induces a metal-to-semiconductor transition.
Electronic band structures depend on magnetic order and spin-orbit coupling.
Phonon modes involving Cr atoms are sensitive to magnetic ordering.
Abstract
We apply the density-functional theory to study various phases (including non-magnetic (NM), anti-ferromagnetic (AFM), and ferromagnetic (FM)) in monolayer magnetic chromium triiodide (CrI), a recently fabricated 2D magnetic material. It is found that: (1) the introduction of magnetism in monolayer CrI gives rise to metal-to-semiconductor transition; (2) the electronic band topologies as well as the nature of direct and indirect band gaps in either AFM or FM phases exhibit delicate dependence on the magnetic ordering and spin-orbit coupling; and (3)the phonon modes involving Cr atoms are particularly sensitive to the magnetic ordering, highlighting distinct spin-lattice and spin-phonon coupling in this magnet. First-principles simulations of the Raman spectra demonstrate that both frequencies and intensities of the Raman peaks strongly depend on the magnetic ordering. The…
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.
Taxonomy
Topics2D Materials and Applications · Heusler alloys: electronic and magnetic properties · Machine Learning in Materials Science
