A team of scientists from Ames National Laboratory has developed a new machine learning model for discovering critical-element-free permanent magnet materials. The model predicts the Curie temperature of new material combinations. It is an important first step in using artificial intelligence to predict new permanent magnet materials. This model adds to the team's recently developed capability for discovering thermodynamically stable rare earth materials. The work is published in Chemistry of Materials.
, electric vehicles, and magnetic refrigeration. These magnets contain critical materials such as cobalt and rare earth elements like neodymium and dysprosium. These materials are in high demand but have limited availability. This situation is motivating researchers to find ways to design new magnetic materials with reduced critical materials.. It is driven by computer algorithms that use data and trial-and-error algorithms to continually improve its predictions.
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 a ML method can save time and resources.
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