A new roadmap details three pathways to developing quantum materials that operate at room temperature. These materials could lead to more energy-efficient computing. Researchers from the University of Ottawa and the Massachusetts Institute of Technology (MIT) published this comprehensive review.
The review focuses on magnetic topological materials. These materials combine magnetism and topology, a mathematical concept describing shapes that cannot be continuously deformed into one another. This unique combination protects electron flow in ways conventional materials cannot. The study summarizes over two decades of global research in this field.
Hang Chi, Canada Research Chair in Quantum Electronic Devices and Circuits, explained that these materials offer a platform where magnetism and quantum physics interact. The review identifies four main families of these materials. It also highlights their quantum effects and potential technological applications.
One significant effect is the quantum anomalous Hall effect. This allows electrical current to flow along material edges with minimal energy loss, even without an external magnetic field. Achieving this reliably and efficiently is a long-standing goal. Chi noted that these materials could enable electrical current or voltage-induced magnetization switching with efficiencies far exceeding conventional metals.
Currently, these effects only occur at temperatures near absolute zero. The primary challenge is to make these materials function at room temperature. The roadmap proposes three strategies: using computational screening and artificial intelligence to evaluate candidate materials, engineering new material combinations in thin layers, and discovering entirely new families of magnetic topological materials.
These advancements could transform computing. They offer a different approach to moving and storing information, potentially leading to cooler, faster, and more energy-efficient devices. Beyond traditional computing, these materials show promise for artificial intelligence hardware, which could reduce the significant energy consumption of AI data centers.
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