Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials

• Published in: Chemistry of materials 2023-08, Vol.35 (16), p.6304-6312
• Main Authors: Singh, Prashant, Del Rose, Tyler, Palasyuk, Andriy, Mudryk, Yaroslav
• Format: Article
• Language: English
• Subjects: electronic structure; elements; magnetic materials; magnetic properties; materials; MATERIALS SCIENCE
• ISSN: 0897-4756; 1520-5002
• DOI: 10.1021/acs.chemmater.3c00892

High-performance permanent magnets with a high Curie temperature, containing less critical materials, are integral to zero-carbon energy solutions. We built a machine-learning model trained over available experimentally measured Curie temperature values to predict the T C of multicomponent magnetic materials. We chose two compositions from a pseudo-binary (Zr1–x Ce x )­Fe2 system, namely, (Zr0.16Ce0.84)­Fe2 and (Zr0.94Ce0.06)­Fe2, to experimentally validate the ability of our model to predict the Curie temperature of novel compounds. We also provided a detailed discussion on the correlation of the Curie temperature with the de Gennes scaling factor in rare-earth intermetallic compounds and its breakdown below a certain rare-earth content. The electronic structure calculations (density of states and Fermi surface) were performed using the density functional theory on selected compounds (Zr0.16Ce0.84)­Fe2 and (Zr0.94Ce0.06)­Fe2 to understand the electronic origin of a strong magnetic exchange. We found that the change in the electronic density of states and electron/hole fillings at the Fermi level directly correlate with the Curie temperature. Notably, our model was able to capture these key electronic structure trends, which show that physics-informed machine learning can play a crucial role in designing new high-performance magnets with improved properties for environmentally sustainable applications.