A team of scientists from Ames National Laboratory in the US, operated by Iowa State University, say they have developed a new machine learning model for discovering critical-element-free permanent magnet materials. According to the research, the model predicts the Curie temperature of new material combinations. This adds to the team’s recently developed capability for discovering thermodynamically stable rare earth materials.
The team used experimental data on Curie temperatures and theoretical modeling to train the 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,” said Yaroslav Mudryk, a scientist at Ames Lab and senior leader of the research team. “This aspect is critical for the design of not only permanent magnets but other functional magnetic materials.”
Mudryk noted that discovering new materials is a challenging activity because research is traditionally based on costly and time-consuming experimentation. The hope is that using an ML method can save time and resources. The team trained its ML model using experimentally known magnetic materials. The information about these materials established a relationship between several electronic and atomic structure features and Curie temperature. These patterns then provided a starting point for finding potential candidate materials.
The model was tested using compounds of cerium, zirconium and iron. This idea was proposed by Andriy Palasyuk, a scientist at Ames Lab and a member of the research team. He wanted to focus on unknown magnet materials based on earth-abundant elements. “The next super magnet must not only be superb in performance but also rely on abundant domestic components,” he noted.
Palasyuk worked with Tyler Del Rose, another scientist at Ames Lab and a member of the research team, to synthesize and characterize the alloys. They found that the ML model was successful in predicting the Curie temperature of material candidates.
This research is discussed further in Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials, written by Prashant Singh, Tyler Del Rose, Andriy Palasyuk and Yaroslav Mudryk, and published in Chemistry of Materials.
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