Machine Learning Model Offers More Sustainable Approach to Discover New Magnet Materials

Ames Lab scientists developed a machine learning model to predict new magnet materials without scarce elements. This offers a more sustainable approach for future technologies.
The model focuses on predicting a material's Curie temperature, which indicates if it can sustain magnetism at high temperatures.
High-performance magnets are essential for things like wind turbines and EVs, but rely on scarce elements like cobalt and rare earth metals.
The model was trained on experimental data to establish relationships between electronic structure and Curie temperature.
The model successfully predicted the Curie temperature of magnet material candidates, showing promise for accelerated discovery.

Ames Lab scientists developed a machine learning model to predict new magnet materials without scarce elements. This offers a more sustainable approach for future technologies.
The model focuses on predicting a material's Curie temperature, which indicates if it can sustain magnetism at high temperatures.
High-performance magnets are essential for things like wind turbines and EVs, but rely on scarce elements like cobalt and rare earth metals.
The model was trained on experimental data to establish relationships between electronic structure and Curie temperature.
The model successfully predicted the Curie temperature of magnet material candidates, showing promise for accelerated discovery.