{"id":460220,"date":"2026-06-04T07:21:53","date_gmt":"2026-06-04T07:21:53","guid":{"rendered":"https:\/\/www.newsbeep.com\/nz\/460220\/"},"modified":"2026-06-04T07:21:53","modified_gmt":"2026-06-04T07:21:53","slug":"ames-lab-scientist-provides-ai-driven-roadmap-for-future-permanent-magnet-design","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/nz\/460220\/","title":{"rendered":"Ames Lab Scientist Provides AI-Driven Roadmap for Future Permanent Magnet Design"},"content":{"rendered":"<p>Newswise \u2014 Researchers at Ames National Laboratory are advancing the discovery of materials for rare-earth-free permanent magnets by combining fundamental physics with <a class=\"word_1780511771960\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">artificial intelligence<\/a>.\u00a0 The work contributes to the U. S. Department of Energy\u2019s (DOE) <a href=\"https:\/\/www.energy.gov\/undersecretaryforscience\/genesis-mission\/genesis-mission\" rel=\"nofollow noopener\" target=\"_blank\">Genesis Mission<\/a>, which unites DOE National Labs, industry, and academia to harness <a class=\"word_1780511771960\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">artificial intelligence<\/a> (<a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a>) for scientific breakthroughs in energy, discovery science, and national security, including a focus on securing America\u2019s supply of rare earths and other critical minerals.<\/p>\n<p>Rare earth elements give today\u2019s high-performance magnets their exceptional strength and resistance to de-magnetization, properties essential for many technologies and particularly for energy generation and defense. But they can be quite expensive and often require dependence on unstable sources outside the United States. Reducing or eliminating the need for rare earth elements will lower costs and enhance domestic production of high-performance magnets.<\/p>\n<p>For more than 20 years, researchers have been looking for permanent magnet materials that do not use rare earth elements. Investigations have largely been material-focused, with researchers relying on a trial-and-error approach that involves synthesizing materials in the lab, testing them, and then using the data to build knowledge one data point at a time.<\/p>\n<p>In a <a href=\"https:\/\/advanced.onlinelibrary.wiley.com\/doi\/10.1002\/adfm.202525433\" rel=\"nofollow noopener\" target=\"_blank\">recently published article<\/a>, Ames Lab Scientist Prashant Singh outlines a faster, more systematic path by combining physics-based modeling, high-throughput simulations, and reasoning-based <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> tools to guide discovery before materials are made in the lab.<\/p>\n<p>This approach focuses on understanding how a material\u2019s atomic structure and electronic behavior to determine key magnetic properties such as magnetization strength, energy storage capacity, resistance to demagnetization, and performance at elevated temperatures. By embedding that physics knowledge into computational models, researchers can more efficiently identify promising material candidates and reduce the need for costly experimental iteration.<\/p>\n<p>Ames Lab researchers are well-positioned as leaders in addressing this challenge.<\/p>\n<p>&#8220;Ames Lab&#8217;s strength comes from its deep expertise and a long history of data in the magnet space that no other institution has,&#8221; Singh said. &#8220;That&#8217;s what makes our role critical. In any material design problem, you need to know how combining two elements will change their performance before you ever run an experiment. We have been building both theoretical and analytical tools to answer that question, and we are now bringing <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> into that process to make it faster and broader in scope.&#8221;<\/p>\n<p>Building on this foundation, the team is developing new <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a>-assisted ways for researchers to interact with these models more directly, allowing them to pose design questions, refine requirements, and explore potential materials more efficiently. These emerging capabilities draw on earlier work such as Singh\u2019s <a href=\"https:\/\/www.ameslab.gov\/news\/ductgpt-demonstrates-how-ai-can-accelerate-discovery-of-next-generation-fusion-materials\" rel=\"nofollow noopener\" target=\"_blank\">recently developed tool DuctGPT<\/a>, an agentic <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> designed for interactive exploration and materials design.<\/p>\n<p>The challenge to making these approaches effective is ensuring <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> models are trained in the right kind of data. Rather than relying on generalized data, models need to be trained on experimentally measured and scientifically calculated material properties to enable predictions that remain grounded in real-world behavior.<\/p>\n<p>\u201cUnderstanding the physics of materials is important to include in <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> frameworks when you are trying to design new materials. If you just use the data to train your models, you are going to get only the predictions within the range of information you have,\u201d Singh said. \u201cBut once you understand the physics of what controls specific properties, then you and your agentic tools or <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> frameworks can search arbitrary material space.\u201d\u00a0<\/p>\n<p>These <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> tools offer an important addition benefit: they can take into consideration material availability and costs.<\/p>\n<p>&#8220;Supply chain conditions shift by the hour, material costs fluctuate, availability changes daily, and the market never stands still,&#8221; Singh explained. &#8220;By factoring those conditions into the discovery process, we can better target materials that are not only high-performing but also practical to produce and scale.\u201d<\/p>\n<p>This roadmap addresses the full critical materials pipeline from accelerating discovery to ensuring industrial viability could reduce America\u2019s reliance on outside sources for critical materials and build a more resilient domestic supply chain. By combining Ames National Laboratory\u2019s long-standing strengths in theory, simulation, and proprietary magnetic materials data with emerging <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> capabilities, researchers hope to dramatically expand the pace and scope of magnetic materials innovation.<\/p>\n<p>&#8220;What sets Ames apart is seven decades of work in critical materials\u2014a depth of knowledge no other lab can match,&#8221; said Singh. &#8220;We have strong foundations in theory and simulation, and proprietary magnetic material data exclusive to Ames. Adding <a class=\"word_1780511771961\" href=\"https:\/\/www.energy.gov\/science\/doe-explainsartificial-intelligence\" rel=\"nofollow noopener\" target=\"_blank\">AI<\/a> to that base lets us explore new materials and magnetic possibilities faster and directly address the supply chain vulnerabilities the country faces.&#8221;<\/p>\n<p>\u00a0<\/p>\n<p><a tabindex=\"-1\" href=\"http:\/\/www.ameslab.gov\/\" rel=\"nofollow noopener\" target=\"_blank\">Ames National Laboratory<\/a>\u00a0is a\u00a0<a tabindex=\"-1\" href=\"https:\/\/www.energy.gov\/\" rel=\"nofollow noopener\" target=\"_blank\">U.S. Department of Energy<\/a>\u00a0<a tabindex=\"-1\" href=\"http:\/\/science.energy.gov\/%22%20\/\" rel=\"nofollow noopener\" target=\"_blank\">Office of Science<\/a>\u00a0National Laboratory operated by Iowa State University. Ames Laboratory creates innovative materials, technologies, and energy solutions. We use our expertise, unique capabilities, and interdisciplinary collaborations to solve global problems.<\/p>\n<p>Ames Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit\u00a0<a tabindex=\"-1\" href=\"https:\/\/energy.gov\/science\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/energy.gov\/science<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Newswise \u2014 Researchers at Ames National Laboratory are advancing the discovery of materials for rare-earth-free permanent magnets by&hellip;\n","protected":false},"author":2,"featured_media":364143,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[233553,2057,204674,363,31264,1667,2293,111,2055,139,69,393,147],"class_list":["post-460220","post","type-post","status-publish","format-standard","has-post-thumbnail","category-science","tag-ai-materials-discoveryagentic-aipermanent-magnet-materials","tag-all-journal-news","tag-ames-national-laboratory","tag-artificial-intelligence","tag-doe-science-news-source","tag-energy","tag-materials-science","tag-new-zealand","tag-newswise","tag-newzealand","tag-nz","tag-physics","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/460220","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=460220"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/460220\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/364143"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=460220"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=460220"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=460220"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}