Scientists at the University of New Hampshire are using artificial intelligence to accelerate the search for advanced magnetic materials. Their work produced a searchable resource containing 67,573 magnetic compounds, including 25 materials that had not previously been recognized as magnets capable of remaining magnetic at high temperatures.
“By accelerating the discovery of sustainable magnetic materials, we can reduce reliance on rare earth elements, lower the cost of electric vehicles and renewable energy systems, and strengthen the U.S. manufacturing base,” said Suman Itani, lead author and doctoral student in physics.
A massive database of magnetic materials
The new resource, called the Northeast Materials Database, makes it easier for scientists to explore materials essential to modern technology. Magnets are essential components of smartphones, medical devices, generators, electric vehicles and many other everyday systems. However, today’s strongest magnets rely on rare earth elements that are expensive, widely imported and increasingly difficult to obtain. Despite the large number of known magnetic compounds, no entirely new permanent magnets have been identified in this pool.
The study, published in Natural communicationsdescribes how the team developed an AI system capable of reading scientific articles and extracting important experimental data. This information was then used to train computer models to determine whether a material is magnetic and to calculate the temperature at which it loses its magnetism. The results have been organized into a comprehensive, searchable database.
Reduce the need for rare earth elements
Researchers have long understood that many magnetic materials likely remain unknown. Yet testing all possible combinations of elements, which could number in the millions, would take a huge amount of time and money in the laboratory.
“We are tackling one of the toughest challenges in materials science – discovering sustainable alternatives to permanent magnets – and we are optimistic that our experimental database and growing AI technologies will make this goal achievable,” said Jiadong Zang, professor of physics and co-author.
Expanding the role of AI in science and education
The research team also includes co-author Yibo Zhang, a postdoctoral researcher in physics and chemistry. Looking ahead, scientists believe the large language model used in this project could serve purposes beyond building this database, particularly in higher education. For example, the technology could convert images into modern rich text formats, helping to update and preserve library collections.
The project received support from the Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, US Department of Energy.