Researchers Unlock Moiré Spintronics in Twisted Van Der Waals Materials for Novel Two-dimensional Magnetism

Twisted van der Waals materials represent a rapidly developing frontier in materials science, offering exciting possibilities for next-generation spintronic devices. Fengjun Zhuo from Zhejiang University, Zhenyu Dai from the University of Houston, and Hongxin Yang, also of Zhejiang University, alongside Zhenxiang Cheng from the University of Wollongong, present a comprehensive overview of recent advances in this field, known as moiré spintronics. Their work explores how twisting these layered materials creates unique magnetic properties and emergent phenomena, including novel spin textures and interactions, that were previously unattainable. Importantly, the researchers highlight the power of machine learning to accelerate the discovery and design of new multifunctional materials for moiré spintronics, potentially revolutionising data storage and processing technologies.

Van der Waals (vdW) materials are increasingly important for exploring novel quantum phenomena and engineering new material properties in two dimensions, potentially revolutionising spintronics. Recent research focuses on moiré spintronics within twisted vdW materials, particularly those incorporating two-dimensional magnets, revealing that stacking arrangements significantly influence their magnetic behaviour and create unique spin textures and interactions.

Topological Materials and Spintronic Device Exploration

A significant body of research investigates the interplay of topology, spin-orbit coupling, and magnetism to create novel spintronic devices. Researchers are exploring topological insulators and semiconductors, combining them with other materials to realise the quantum anomalous Hall effect for dissipationless edge states, and investigating Weyl and Dirac semimetals for advanced electronic and spintronic applications. A key focus lies on combining magnetism with topological states to create controllable spintronic devices, utilising spin-orbit torque to efficiently manipulate magnetization. Two-dimensional materials, such as graphene and transition metal dichalcogenides, serve as building blocks for topological and spintronic devices.

Computational modelling, employing techniques like Density Functional Theory, plays a crucial role in predicting and understanding material properties, with researchers also leveraging the Rashba and Dresselhaus effects to control spin transport. Beyond spintronics, research extends to the fundamental properties of 2D materials, including their electronic structure, optical properties, and the impact of defects and edge effects. Van der Waals heterostructures, created by stacking different 2D materials, offer a pathway to tailor material properties, with computational materials science remaining central through the development of advanced methods like Density Functional Theory and machine learning algorithms to accelerate materials discovery.

Twisted Layers Reveal Unexpected Magnetic Order

Researchers are uncovering remarkable magnetic properties in twisted van der Waals materials, opening new avenues for spintronic devices. These materials, created by layering two-dimensional magnets with slight twists, exhibit complex magnetic interactions and emergent phenomena, with investigations revealing that the stacking order significantly influences interlayer magnetism and creates unique non-collinear spin textures within the moiré superlattice. The results demonstrate the ability to tailor magnetic properties through precise control of the twist angle and stacking order. Phase diagrams reveal a rich landscape of magnetic configurations, including magnetic bubble lattices and skyrmion lattices, dependent on the strength of interlayer exchange interactions and moiré periodicity, with calculations showing the emergence of stable skyrmion lattices exhibiting switching behaviour under applied magnetic fields.

Moiré Spintronics and Machine Learning Advancements

Recent research highlights the emerging field of moiré spintronics, which explores twisted van der Waals (vdW) materials for potential advancements in spintronic devices. These structures exhibit novel magnetic properties and phenomena, including unique spin textures, interactions, and magnons, with the ability to manipulate these properties through stacking arrangements offering a pathway to design multifunctional materials for future spintronic applications. Machine learning algorithms are being used to create more efficient force fields, develop deep learning models for electronic and magnetic structures, and assist in the inverse design of materials with specific desired properties, promising to expedite the discovery and optimization of moiré spintronic devices. While the field demonstrates significant promise, researchers acknowledge challenges remain in fully understanding and controlling the complex interplay of factors within these twisted structures, with future research likely focusing on refining theoretical models, improving material fabrication techniques, and further developing machine learning tools.