The Discovery of New Materials Just Got a Whole Lot Easier
The discovery of new materials is key to solving some of humanity’s biggest challenges. However, traditional methods of discovering new materials can feel like "finding a needle in a haystack."
Historical Methods of Materials Discovery
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.
Introducing MatterGen
Now, a powerful new generative AI tool from Microsoft 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.
How MatterGen Works
Published in a paper in Nature, Microsoft 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 colors, MatterGen generates material structures by altering elements, positions, and periodic lattices in randomized structures. This bespoke architecture is designed specifically to handle the unique demands of materials science, such as periodicity and 3D arrangements.
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 behavior, or combinations of these constraints. The model was trained using over 608,000 stable materials compiled from the Materials Project and Alexandria databases.
Comparison with Traditional Screening Methods
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.
Experimental Synthesis of Novel Material
To prove MatterGen’s potential, Microsoft collaborated with researchers at Shenzhen Institutes of Advanced Technology (SIAT) – part of the Chinese Academy of Sciences – to experimentally synthesize a novel material designed by the AI.
Conclusion
Microsoft positions MatterGen as a complementary tool to its previous AI model, MatterSim, 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.
Frequently Asked Questions
Q: What is MatterGen?
A: MatterGen is a generative AI tool that directly engineers novel materials based on design requirements, offering a potentially game-changing approach to materials discovery.
Q: How does MatterGen work?
A: MatterGen operates within the 3D geometry of materials, generating material structures by altering elements, positions, and periodic lattices in randomized structures.
Q: How does MatterGen compare to traditional screening methods?
A: MatterGen significantly outperformed traditional screening methods in generating novel materials with specific properties.
Q: Has MatterGen been experimentally proven?
A: Yes, Microsoft collaborated with researchers at Shenzhen Institutes of Advanced Technology (SIAT) – part of the Chinese Academy of Sciences – to experimentally synthesize a novel material designed by the AI.
