A research team at Tokyo University of Science has succeeded in doing something materials scientists have been pursuing for decades: creating bulk ferromagnetic quasicrystals that can be annealed and studied in detail. Groundbreaking results reported in Journal of the American Chemical Societybreaking new ground in magnetic materials research and ultimately potentially impacting the next generation of sensing, computing, and quantum technologies.
The significance of this result lies not only in the creation of new materials, but also in the advanced technology used to discover them. Researchers have demonstrated that by combining machine learning and experimental metallurgy, artificial intelligence is becoming a powerful tool for materials discovery.
What is a quasicrystal?
Quasicrystals occupy an unusual position in materials science. Unlike conventional crystals, which exhibit repeating atomic patterns, quasicrystals exhibit long-range order without periodic repeats. This allows unique symmetries, such as quintuple and icosahedral arrays, that are not possible with regular crystals.
Quasicrystals have fascinated scientists since their discovery in the 1980s. Because quasicrystals challenge traditional thinking about how matter is organized. Their unusual atomic structures produce unusual mechanical, thermal, and electronic properties. However, understanding their magnetic behavior remains difficult.
While ferromagnetism, the phenomenon that allows materials to act as permanent magnets, has been widely studied in conventional crystalline solids and amorphous materials, quasicrystals have largely remained outside this field. Ferromagnetic quasicrystals have been produced before, but only by rapid quenching techniques that produce metastable materials with structural defects. These limitations have prevented researchers from systematically studying its unique magnetic properties.
The Tokyo team took a technology-driven approach to this problem. Rather than relying solely on trial-and-error experiments, the researchers employed a machine learning-based phase classifier to predict promising alloy compositions. Utilizing data from the HYPOD-X quasicrystal database and other materials datasets, the algorithm evaluated hundreds of potential candidates.
The model generated 675 potential quinary alloy systems and identified gold-copper-aluminum-indium alloys containing rare earth elements as the most promising route to stable ferromagnetic quasicrystals.
Using this guidance, the researchers synthesized three new materials including gadolinium (Gd), terbium (Tb), and dysprosium (Dy). Importantly, the material was manufactured using standard arc melting techniques, followed by controlled annealing rather than rapid quenching. This resulted in structurally consistent quasicrystals with unprecedented thermal stability.
As explained by Professor Takashi Tamura, machine learning-assisted strategies have enabled the development of ferromagnetic icosahedral quasicrystals with excellent structural quality, allowing for the first time a systematic study of their intrinsic magnetic behavior.
Revealing hidden magnetic behavior
The ability to anneal and stabilize these materials has transformed them from scientific curiosities into bona fide research platforms. Tests revealed that all three quasicrystals exhibited long-range ferromagnetic ordering at temperatures ranging from 9.7 Kelvin to 28.3 Kelvin. This provides clear evidence that stable ferromagnetism can exist within highly ordered quasiperiodic structures.
More interestingly, the research team observed two distinctly different forms of magnetic critical behavior. Terbium- and dysprosium-based materials exhibited behavior consistent with mean-field ferromagnetism, a model that effectively features long-range magnetic interactions. In contrast, gadolinium-containing materials exhibited behavior associated with shorter-range magnetic interactions and stronger spin fluctuations.
These findings suggest that the magnetic criticality of quasicrystals is governed by a combination of quasi-periodic atomic order and magnetic spin symmetry. This relationship has not previously been investigated in such detail because suitable materials did not exist.
Why tech companies should pay attention
At first glance, materials operating below 30 Kelvin may appear to have limited practical relevance. However, history shows that fundamental advances in magnetism often underpin future technological revolutions.
Advanced magnetic materials are essential for a wide range of technologies, including high-density data storage, spintronics, quantum computing, magnetic sensing systems, and energy conversion devices. Understanding how quasi-periodic structures affect magnetic interactions could lead to entirely new classes of functional materials with tunable properties.
The research also focuses on a broader trend transforming materials science: the integration of artificial intelligence into discovery pipelines. Machine learning systems are increasingly being used to identify promising compounds, predict material properties, and reduce the time needed to move from theoretical concept to experimental validation.
In traditional materials development, discovering new alloys can require years of screening and testing. The AI-driven approach significantly narrows the search scope and allows researchers to focus on the most promising candidates. The success of the Tokyo University of Science team shows how computational intelligence and laboratory experiments can work together to accelerate innovation.
