Topological quasiparticles can be found in chiral magnetic materials with antisymmetric exchange interactions. They are formed by nanoscale spin textures, including but not limited to skyrmions, bimerons, and hopfions. Their particle-like nature, chiral mobility, and topologically protected stability make them highly promising candidates for next-generation information storage and computing technologies, such as nonvolatile in-memory computing and neuromorphic systems. Information, whether classical (binary) or quantum (qubits), can be encoded in the various degrees of freedom of topological quasiparticles. Recent experimental and theoretical advances have highlighted their great potential for unconventional applications, which may contribute to the development of future artificial-intelligence (AI) hardware.

In this project, students will study the fundamental theories needed to understand topological quasiparticles in magnetic systems with chiral exchange interactions. They will also explore the rich and complex dynamics of topological quasiparticles driven by various external stimuli, such as electric currents and fields, while investigating unconventional applications based on these quasiparticles. The project will involve fundamental micromagnetic theory, CPU-based and GPU-accelerated modelling of magnetic systems, the design and development of magnetic and spintronic device applications, and the implementation of AI functionalities based on magnetic systems.