AI helps researchers design rare-earth-free magnets

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In a paper published in the journal Chemistry of Materials, scientists explain that high-performance magnets are essential for technologies such as wind power, data storage, electric vehicles and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements such as neodymium and dysprosium, which are in high demand but have limited availability.

To find ways to design new magnets with less critical materials, the Ames group used experimental data on Curie temperatures and theoretical modeling to train a machine learning (ML) algorithm.

“Finding compounds with a high Curie temperature is an important first step in the search for materials that can retain magnetic properties at elevated temperatures,” lead researcher Yaroslav Mudrik said in a media statement, “not only is this matter important for the design of permanent magnets. But other functional magnetic material.”

According to Mudrick, discovering new materials is a challenging activity because discovery has traditionally been based on experiments, which are expensive and time-consuming. However, using the ML method can save time and resources.

With this in mind, the team trained their ML model using experimentally known magnetic materials. Information on these materials establishes a relationship between several electronic and atomic structure features and the Curie temperature. These patterns provide a basis for the computer to search for potential candidate materials.

To test the model, the team used compounds based on cerium, zirconium and iron.

“The next supermagnet should not only excel in performance, but also rely on abundant household components,” said Andriy Palasyuk, co-author of the study.

Palasyuk worked with Tyler Del Rose, another scientist at the Ames lab, to synthesize and characterize the alloys. They found that the ML model was successful in predicting the Curie temperature of the material candidates. This breakthrough is an important first step in creating a high-throughput way to design new permanent magnets for future technological applications.

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