# Ames Lab Develops Physics-Informed AI (DuctGPT) to Discover Rare-Earth-Free Magnets

> Researchers at Ames National Laboratory are using AI to attack a stubborn materials problem: designing powerful permanent magnets that do not depend on rare-earth elements, whose supply is concentrated and geopolitically fragile. Their approach, nicknamed DuctGPT, does not just let a model guess. It fuses physics-based modeling and high-throughput simulation with reasoning AI, so the system explores candidate materials within the constraints of what physics actually allows. That combination is the point. A pure language model can hallucinate a compound that could never exist, but binding the AI to physical laws and simulated properties lets it propose realistic candidates that are worth the expense of synthesizing and testing in a lab. It turns an open-ended search into a guided one. The work is part of the Department of Energy's Genesis Mission for AI-driven science, and it aims squarely at a US supply-chain vulnerability in critical minerals. If it pans out, the broader lesson is about method: coupling AI with hard scientific constraints, rather than turning it loose on data alone, may be how machine learning earns its keep in the physical sciences.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Ames National Laboratory · Published Thursday, July 16, 2026_

## Wortins' read

Researchers at Ames National Laboratory are using AI to attack a stubborn materials problem: designing powerful permanent magnets that do not depend on rare-earth elements, whose supply is concentrated and geopolitically fragile. Their approach, nicknamed DuctGPT, does not just let a model guess. It fuses physics-based modeling and high-throughput simulation with reasoning AI, so the system explores candidate materials within the constraints of what physics actually allows. That combination is the point. A pure language model can hallucinate a compound that could never exist, but binding the AI to physical laws and simulated properties lets it propose realistic candidates that are worth the expense of synthesizing and testing in a lab. It turns an open-ended search into a guided one. The work is part of the Department of Energy's Genesis Mission for AI-driven science, and it aims squarely at a US supply-chain vulnerability in critical minerals. If it pans out, the broader lesson is about method: coupling AI with hard scientific constraints, rather than turning it loose on data alone, may be how machine learning earns its keep in the physical sciences.

## Source

[Read the full story at Ames National Laboratory](https://www.ameslab.gov/news/ames-lab-scientist-provides-ai-driven-roadmap-for-future-permanent-magnet-design)

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