Alqem AI & MPI CPfS Launch Project for New Magnetic Materials

Alqem AI is launching a project to identify new magnetic materials without relying on rare earth elements, addressing increasing geopolitical vulnerabilities in critical raw material supply; the company reports that high-performance permanent magnets, essential for electric vehicles and wind turbines, are currently produced at a rate of about 90 percent in China. Despite bronze, iron, steel, and silicon driving past technological eras, hundreds of millions of theoretically possible crystalline compounds remain undiscovered, prompting Alqem AI to leverage artificial intelligence in the search for alternatives. This effort receives support from Claudia Felser, director at the Max Planck Institute for Chemical Physics of Solids, and benefits from a unique connection; Julia Krez, VP of Operations at Alqem AI, earned her Ph. D. under Felser and is also a member of the Board of Trustees. Hanh Nguyen, CEO of Alqem AI, says, “We’re starting where the need is greatest: with rare-earth-free magnets—a product the world urgently needs, since rare earths have remained irreplaceable for decades.”

The DEEPtech startup, fresh off an 8 million euro pre-seed funding round, is leveraging AI to accelerate materials discovery, with an initial focus on a critical need: rare-earth-free magnets. This project addresses a growing geopolitical concern, as approximately 90 percent of high-performance permanent magnets, essential components in electric vehicles, wind turbines, and defense systems, are currently produced in China, and recent export restrictions have highlighted supply chain vulnerabilities. Alqem AI’s approach centers on two proprietary data foundations: an extensive database of predicted materials and high-quality training data for material properties, a combination the company believes will bridge the gap between computational prediction and real-world synthesis. This collaboration builds on the Max Planck Society’s long-standing research in quantum materials and its established partnerships with industry.

Max Planck Society Collaboration on Quantum Materials

Alqem AI is addressing this complexity with an innovative platform that merges extensive material prediction databases with high-quality training data and laboratory synthesis capabilities, effectively translating digital predictions into tangible materials. Central to this partnership is a project focused on identifying new magnetic materials that circumvent the need for rare earth elements, a critical consideration given that approximately 90 percent of high-performance permanent magnet production currently occurs in China. Recent export restrictions have elevated the security of critical raw material supply to a key geopolitical issue, prompting a search for alternative materials. Claudia Felser leads this project and is also a member of the Board of Trustees of the Max Planck Institute for Chemical Physics of Solids; this dual role underscores the deep integration between the startup and the research institute.

The collaboration isn’t solely focused on computational prediction; Claudia Felser’s team has a history of synthesizing and characterizing rare-earth-free magnets, and is now applying rigorous experimental testing to candidates identified by Alqem AI’s platform. Felser states, “Discovering a truly new permanent magnet is one of the most difficult problems in materials science,” and adds, “the last real breakthrough was over forty years ago.” She emphasizes the power of combining large-scale computer-aided screening with systematic synthesis, believing this approach holds significant promise for materials discovery.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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