Year: 2025
Type: article
Abstract: Twisted magnetic van der Waals materials offer a promising route for multiferroic engineering, yet modeling large-scale moiré superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled systems, we develop a comprehensive interatomic machine learning potential and apply it to twisted bilayer NiI_{2}. Structural relaxation introduces moiré-periodic "bumps" that modulate the interlayer spacing by about 0.55 Å and in-plane ionic shifts up to 0.48 Å. Concurrently, our machine learning potential, which faithfully captures all key spin interactions, produces reliable magnetic configurations; combined with the more accurate generalized Katsura-Nagaosa-Balatsky mechanism, it delivers precise spin-driven polarization. For twist angles 1.89°≤θ≤2.45°, both mechanisms become prominent, yielding rich polarization textures that combine ionic out-of-plane dipoles with purely electronic in-plane domains. In the rigid (unrelaxed) bilayer, skyrmions are absent; lattice relaxation is thus essential for generating polar-magnetic topologies. In contrast, near θ≈60°, stacking-dependent ferroelectric displacements dominate, giving rise to polar meron-antimeron networks. These results reveal cooperative ionic and spin-driven ferroelectricity in twisted bilayer NiI_{2}, positioning twisted van der Waals magnets as adaptable platforms for tunable multiferroic devices.
Source: Physical Review Letters
DOI: 10.1103/p6n8-n1rx
Institutions: Fudan University, State Key Laboratory of Surface Physics
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Shanghai Education Development Foundation, Fudan University, Science and Technology Commission of Shanghai Municipality, National Key Research and Development Program of China, Basic and Applied Basic Research Foundation of Guangdong Province
OpenAlex ID: W4415926286