Collaboration with MatNex helps reduce reliance on REE

British firm, Materials Nexus (operating as MatNex) has developed an AI-powered materials discovery tool that combines first-principles physics and machine learning to accelerate the design of sustainable, high-performance materials. Earlier work used the platform to guide development of rare-earth-lean and rare-earth-free magnets (including work under the “MagNex” banner). With an initial focus on magnets, the platform has screened well over 100 million compositions to address industry needs around supply-chain security, cost, performance and environmental impact—compressing discovery timelines that historically took decades.

The project demonstrated that combining physics-based modelling with machine learning and tightly coupled experimentation can shorten development cycles dramatically while improving the quality of candidates that reach the lab. By including guidance on likely phases and processing windows, the platform reduced dead-ends and focused experimental time on alloys with the best chance of success. The additive-manufacturing step provided early insights into scalability and process robustness, bridging the gap between small-scale samples and practical components.

Building on the DREAM results, the partners plan to scale up the most promising composition and extend the platform to a wider set of chemistries and processing routes. We will also strive to tighten the feedback loop between prediction and proof. The end goal is to deliver manufacturable, rare-earth-free magnet options to industry at a lower cost and with a smaller environmental footprint.