Ames researchers develop machine-learning model to find new magnetic materials without critical elements
A team of scientists from Ames National Laboratory is developing a new machine-learning (ML) model for discovering critical-element-free permanent magnet materials. The model predicts the Curie temperature of new material combinations. Curie temperature is the maximum temperature at which a material maintains its magnetism. A paper on the work is published in the ACS journal Chemistry of Materials.
The researchers said that this 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, data storage, 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.
The team used experimental data on Curie temperatures and theoretical modeling to train the ML algorithm. 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.
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. This aspect is critical for the design of not only permanent magnets but other functional magnetic materials.—Yaroslav Mudryk, a scientist at Ames Lab and senior leader of the research team
To test the model, the team used compounds based on cerium, zirconium, and iron.
Prashant Singh, Tyler Del Rose, Andriy Palasyuk, and Yaroslav Mudryk (2023) “Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials” Chemistry of Materials 35 (16), 6304-6312 doi: 10.1021/acs.chemmater.3c00892