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Researchers at the US’s Ames National Laboratory have developed a new machine-learning model for discovering critical-element-free permanent magnet materials.
The model predicts the Curie temperature – the maximum temperature at which an element maintains its magnetism – of new material combinations and it is considered an important first step in using artificial intelligence to predict new permanent magnet materials.
In a paper published in the journal Chemistry of Materials, the scientists explain that high-performance magnets are essential for technologies such as wind energy, data storage, electric vehicles, and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements like neodymium and dysprosium, which are in high demand but have limited availability.
To figure out ways to design new magnets with reduced critical materials, the Ames group used experimental data on Curie temperatures and theoretical modelling to train the machine learning (ML) algorithm.
“Finding compounds with the high Curie temperature is an important first step in the discovery of materials that can sustain magnetic properties at elevated temperatures,” lead researcher Yaroslav Mudryk said in a media statement “This aspect is critical for the design of not only permanent magnets but other functional magnetic materials.”
According to Mudryk, discovering new materials is a challenging activity because the search is traditionally based on experimentation, which is expensive and time-consuming. However, using an ML method can save time and resources.
With this in mind, the team trained their ML model using experimentally known magnetic materials. The information about these materials establishes a relationship between several electronic and atomic structure features and Curie temperature. These patterns give the computer a basis for finding potential candidate materials.
To test the model, the team used compounds based on cerium, zirconium, and iron.
“The next super magnet must not only be superb in performance but also rely on abundant domestic components,” Andriy Palasyuk, co-author of the study, said.
Palasyuk worked with Tyler Del Rose, another scientist at Ames Lab, to synthesize and characterize the alloys. They found that the ML model was successful in predicting the Curie temperature of material candidates. This success is an important first step in creating a high-throughput way of designing new permanent magnets for future technological applications.
