Join our daily and weekly newsletters for the latest updates and exclusive content on top AI coverage. More information
Microsoft Research today unveiled a powerful new artificial intelligence system that generates new materials with specific desired properties, potentially accelerating the development of better batteries, more efficient solar cells and other critical technologies.
The system, called MatterGen, represents a fundamental shift in the way scientists discover new materials. Rather than screening millions of existing compounds—a traditional approach that can take years—MatterGen directly generates new materials based on desired characteristics, similar to how AI image generators create images from textual descriptions.
“Generative models provide a new paradigm for materials design by directly generating entirely new materials with respect to desired property constraints,” said Tian Xie, senior research manager at Microsoft Research and lead author of the study published today in Nature. “This represents significant progress toward creating a universal generative model for materials design.”
How Microsoft’s AI engine works differently than traditional methods
MatterGen uses a specialized type of artificial intelligence called a diffusion model – similar to those behind image generators such as DALL-E – but adapted to work with three-dimensional crystal structures. It gradually refines the random arrangement of atoms into stable, useful materials that meet specified criteria.
The results outperform previous approaches. According to the research paper, materials produced by MatterGen are “more than twice as likely to be novel and stable, and more than 15 times closer to a local energy minimum” compared to previous AI approaches. This means that the materials created are more likely to be useful and physically possible to create.
In one notable demonstration, the team collaborated with scientists from China’s Shenzhen Institutes of Advanced Technology to synthesize a new material, TaCr2O6, that MatterGen designed. The real-world material closely matched the AI predictions and confirmed the practical applicability of the system.
Real-world applications could transform energy storage and computing
The system is particularly notable for its flexibility. It can be “tuned” to generate materials with specific properties – from particular crystal structures to desired electronic or magnetic properties. This could be invaluable in designing materials for specific industrial applications.
The consequences can be far-reaching. New materials are key to advancing technologies in energy storage, semiconductor design and carbon capture. For example, better battery materials could accelerate the transition to electric vehicles, while more efficient solar cell materials could make renewable energy sources more efficient.
“From an industrial perspective, the potential here is huge,” explained Xie. “Human civilization has always depended on material innovation. If we can use generative artificial intelligence to make materials design more efficient, it could accelerate progress in industries like energy, healthcare and more.”
Microsoft’s open source strategy aims to accelerate scientific discovery
Microsoft has released the MatterGen source code under an open source license, allowing researchers around the world to build on the technology. The move could accelerate the system’s impact across different scientific disciplines.
The development of MatterGen is part of Microsoft’s broader AI for Science initiative, which aims to accelerate scientific discovery using AI. The project integrates with Microsoft’s Azure Quantum Elements platform and potentially makes the technology available to businesses and researchers through cloud computing services.
But experts caution that while MatterGen represents significant progress, the path from computationally designed materials to practical applications still requires extensive testing and refinement. The system’s predictions, while promising, require experimental validation before industrial deployment.
Still, the technology represents a significant step forward in the use of artificial intelligence to accelerate scientific discovery. As Daniel Zügner, the project’s lead researcher, noted: “We are deeply committed to research that can have a positive impact in the real world, and this is just the beginning.”
