# DuctGPT: Physics-Trained AI Accelerates Rare-Earth-Free Magnet Discovery

> Researchers at Ames Laboratory have built DuctGPT, an AI aimed at one of the quieter bottlenecks in clean technology: permanent magnets that do not depend on rare earth elements, which are expensive and geopolitically fraught to source. Magnets like these sit inside motors, wind turbines, and countless devices, so a domestically producible alternative would matter well beyond the lab. What makes DuctGPT interesting is how it thinks. Instead of just memorizing patterns in existing experimental data, it is trained on the underlying physics, how alloy composition, electronic structure, elastic properties, and thermodynamics shape a material's ductility and performance. That lets it reason about combinations no one has tested yet, and it has already screened more than a thousand alloy compositions across regions of material space that ordinary machine learning cannot reach. The model also folds in supply chain costs and component sourcing, so its suggestions are meant to be both scientifically sound and practical to manufacture at home. It is a concrete example of physics informed AI doing real discovery work, extrapolating beyond its training data rather than simply interpolating within it.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: Ames Laboratory · Published Saturday, July 11, 2026_

## Wortins' read

Researchers at Ames Laboratory have built DuctGPT, an AI aimed at one of the quieter bottlenecks in clean technology: permanent magnets that do not depend on rare earth elements, which are expensive and geopolitically fraught to source. Magnets like these sit inside motors, wind turbines, and countless devices, so a domestically producible alternative would matter well beyond the lab. What makes DuctGPT interesting is how it thinks. Instead of just memorizing patterns in existing experimental data, it is trained on the underlying physics, how alloy composition, electronic structure, elastic properties, and thermodynamics shape a material's ductility and performance. That lets it reason about combinations no one has tested yet, and it has already screened more than a thousand alloy compositions across regions of material space that ordinary machine learning cannot reach. The model also folds in supply chain costs and component sourcing, so its suggestions are meant to be both scientifically sound and practical to manufacture at home. It is a concrete example of physics informed AI doing real discovery work, extrapolating beyond its training data rather than simply interpolating within it.

## Source

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

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