A team of researchers from the U.S. Department of Energy’s Ames National Laboratory developed a new ML 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. High-performance magnets are essential for technologies such as wind energy and electric vehicles. These magnets contain critical materials such as cobalt and rare earth elements like Neodymium and Dysprosium. The team used experimental data on Curie temperatures and theoretical modelling to train the ML algorithm. To validate the model, the team used compounds based on Cerium, Zirconium, and Iron. They found that the ML model was successful in predicting the Curie temperature of material candidates.