[AI]Microsoft advances materials discovery with MatterGen

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The discovery of new materials is key to solving some of humanity’s biggest challenges. However, as highlighted by

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, traditional methods of discovering new materials can feel like “finding a needle in a haystack.”

Historically, finding new materials relied on laborious and costly trial-and-error experiments. More recently, computational screening of vast materials databases helped to speed up the process, but it remained a time-intensive process.

Now, a powerful new generative AI tool from

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could accelerate this process significantly. Dubbed MatterGen, the tool steps away from traditional screening methods and instead directly engineers novel materials based on design requirements, offering a potentially game-changing approach to materials discovery.

Published in a paper in

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describes MatterGen as a diffusion model that operates within the 3D geometry of materials. Where an image diffusion model might generate images from text prompts by tweaking pixel colours, MatterGen generates material structures by altering elements, positions, and periodic lattices in randomised structures. This bespoke architecture is designed specifically to handle the unique demands of materials science, such as periodicity and 3D arrangements.  

“MatterGen enables a new paradigm of generative AI-assisted materials design that allows for efficient exploration of materials, going beyond the limited set of known ones,” explains

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A leap beyond screening

Traditional computational methods involve screening enormous databases of potential materials to identify candidates with desired properties. Yet, even these methods are limited in their ability to explore the universe of unknown materials and require researchers to sift through millions of options before finding promising candidates.  

In contrast, MatterGen starts from scratch—generating materials based on specific prompts about chemistry, mechanical attributes, electronic properties, magnetic behaviour, or combinations of these constraints. The model was trained using over 608,000 stable materials compiled from the Materials Project and Alexandria databases.

In the comparison below, MatterGen significantly outperformed traditional screening methods in generating novel materials with specific properties—specifically a bulk modulus greater than 400 GPa, meaning they are hard to compress.

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While screening exhibited diminishing returns over time as its pool of known candidates became exhausted, MatterGen continued generating increasingly novel results.

One common challenge encountered during materials synthesis is compositional disorder—the phenomenon where atoms randomly swap positions within a crystal lattice. Traditional algorithms often fail to distinguish between similar structures when deciding what counts as a “truly novel” material.  

To address this,

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devised a new structure-matching algorithm that incorporates compositional disorder into its evaluations. The tool identifies whether two structures are merely ordered approximations of the same underlying disordered structure, enabling more robust definitions of novelty.

Proving MatterGen works for materials discovery

To prove MatterGen’s potential,

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collaborated with researchers at Shenzhen Institutes of Advanced Technology (SIAT) – part of the ******** Academy of Sciences – to experimentally synthesise a novel material designed by the AI.

The material, TaCr₂O₆, was generated by MatterGen to meet a bulk modulus target of 200 GPa. While the experimental result fell slightly short of the target, measuring a modulus of 169 GPa, the relative error was just 20%—a small discrepancy from an experimental perspective.

Interestingly, the final material exhibited compositional disorder between Ta and Cr atoms, but its structure aligned closely with the model’s prediction. If this level of predictive accuracy can be translated to other domains, MatterGen could have a profound impact on material designs for batteries, fuel cells, magnets, and more.

Today in

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: Our MatterGen model represents a paradigm shift in materials design, applying generative AI to create new compounds with specific properties with unprecedented precision.

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— Satya Nadella (@satyanadella)

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positions MatterGen as a complementary tool to its previous AI model,

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, which accelerates simulations of material properties. Together, the tools could serve as a technological “flywheel”, enhancing both the exploration of new materials and the simulation of their properties in iterative loops.

This approach aligns with what

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refers to as the “fifth paradigm of scientific discovery,” in which AI moves beyond pattern recognition to actively guide experiments and simulations.  

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has released MatterGen’s

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under the MIT licence. Alongside the code, the team has made the model’s training and fine-tuning datasets available to support further research and encourage broader adoption of this technology.

Reflecting on generative AI’s broader scientific potential,

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draws parallels to drug discovery, where such tools have already started transforming how researchers design and develop medicines. Similarly, MatterGen could reshape the way we approach materials design, particularly for critical domains such as renewable energy, electronics, and aerospace engineering. 

(Image credit:

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