{"id":110788,"date":"2025-11-11T10:26:40","date_gmt":"2025-11-11T09:26:40","guid":{"rendered":"https:\/\/www.sonnenseite.com\/?p=110788"},"modified":"2025-11-11T10:26:41","modified_gmt":"2025-11-11T09:26:41","slug":"unh-researchers-harness-ai-to-discover-magnetic-materials","status":"publish","type":"post","link":"https:\/\/www.sonnenseite.com\/en\/science\/unh-researchers-harness-ai-to-discover-magnetic-materials\/","title":{"rendered":"UNH Researchers Harness AI to Discover Magnetic Materials"},"content":{"rendered":"\n

New research could reduce dependence on rare-earth elements to power technology<\/p>\n\n\n\n

Magnets are at the core of the technology that powers our world: smartphones, medical devices, power generators, electric vehicles, and more. But these magnets rely on expensive, imported, and increasingly difficult to obtain rare-earth elements, and no new permanent magnet has been discovered from the many magnetic compounds we know exist.<\/p>\n\n\n\n

Now, UNH researchers have harnessed artificial intelligence to accelerate the discovery of new functional magnetic materials. The research, published recently in the journal Nature Communications<\/a>, details the creation of the Northeast Materials Database<\/a> of 67,573 magnetic materials entries, including 25 previously unrecognized compounds that remain magnetic even at high temperatures.<\/p>\n\n\n\n

\u201cBy accelerating the discovery of sustainable magnetic materials, we can reduce dependence on rare-earth elements, lower the cost of electric vehicles and renewable-energy systems, and strengthen the U.S. manufacturing base,\u201d says lead author Suman Itani, a Ph.D. student in physics at UNH.<\/p>\n\n\n\n

Scientists know that many undiscovered magnetic compounds exist, but testing every possible combination of elements \u2014 potentially millions \u2014 in the lab is prohibitively time-consuming and expensive.<\/p>\n\n\n\n

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\u201cBy accelerating the discovery of sustainable magnetic materials, we can reduce dependence on rare-earth elements, lower the cost of electric vehicles and renewable-energy systems, and strengthen the U.S. manufacturing base.\u201d<\/p>\n<\/blockquote>\n\n\n\n

Itani and his co-authors \u2014 physics professor Jiadong Zang,<\/a> who is Itani\u2019s advisor, and Yibo Zhang, a postdoctoral researcher in both physics and chemistry \u2014 built an artificial intelligence system that can read scientific papers and extract those key experimental details. This data fed computer models that identified whether a material is magnetic, and how high a temperature it can withstand before losing its magnetism.<\/p>\n\n\n\n

\u201cCollecting this kind of information by hand would take an enormous amount of effort,\u201d says Itani. \u201cOur AI system can do it quickly and automatically organize everything into a single, searchable database.\u201d<\/p>\n\n\n\n

\u201cWe are tackling one of the most difficult challenges in materials science \u2014 discovering sustainable alternatives to permanent magnets \u2014 and we are optimistic that our experimental database and growing AI technologies will make this goal achievable,\u201d says Zang.<\/p>\n\n\n\n

Going forward, Zang says, the modern large language model behind this project could have widespread use beyond this database, particularly in higher education. For instance, converting images to modern rich text format could also be used to modernize library holdings.<\/p>\n\n\n\n

This work was supported by the Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, U.S. Department of Energy.<\/p>\n\n\n\n