Moir\'e-Induced Magnetoelectricity in Twisted Bilayer NiI2
Haiyan Zhu, Hongyu Yu, Weiqin Zhu, Guoliang Yu, Changsong Xu, Hongjun Xiang

TL;DR
This paper develops a machine learning framework to model large-scale moiré superlattices in twisted bilayer NiI2, revealing how structural relaxation and twist angle influence emergent magnetoelectric phenomena and topological polarization textures.
Contribution
It introduces a SpinGNN++ based interatomic ML potential for twisted bilayer NiI2, enabling accurate simulation of spin-lattice interactions and emergent ferroelectricity in moiré structures.
Findings
Structural relaxation causes moiré-periodic bumps and ionic shifts.
Lattice relaxation is crucial for polar-magnetic topologies.
Twist angle near 60° leads to ferroelectric dislocation networks.
Abstract
Twisted magnetic van der Waals (vdW) materials offer a promising route for multiferroic engineering, yet modeling large-scale moir\'e superlattices remains challenging. Leveraging a newly developed SpinGNN++ framework that effectively handles spin-lattice coupled systems, we develop a comprehensive interatomic machine learning (ML) potential and apply it to twisted bilayer NiI2 (TBN). Structural relaxation introduces moir\'e-periodic "bumps" that modulate the interlayer spacing by about 0.55~\AA{} and in-plane ionic shifts up to 0.48~\AA{}. Concurrently, our ML potential, which faithfully captures all key spin interactions, produces reliable magnetic configurations; combined with the generalized KNB mechanism, it yields accurate spin-driven polarization. For twist angles 1.89^{\circ} \leq \theta \leq 2.45^{\circ}, both mechanisms become prominent, yielding rich polarization textures…
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Taxonomy
Topics2D Materials and Applications · Multiferroics and related materials · Topological Materials and Phenomena
