Microsoft just built an AI that designs materials for the future: here’s how it works

Microsoft Research Today I introduced a powerful new AI system that generates new materials with specific desired properties, which could accelerate the development of better batteries, more efficient solar cells and other vital technologies.

System, called MatterGenIt represents a fundamental shift in how scientists discover new materials. Instead of screening millions of existing compounds – a traditional approach that can take years – MatterGen directly creates new materials based on desired properties, similar to the way AI image generators create images from text descriptions.

“Generative modeling provides a new paradigm for materials design by directly generating completely new materials while taking into account desired property constraints,” said Tian Shih, principal research director at Microsoft Research and lead author of the book. 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 from traditional methods

MatterGen uses a specialized type of artificial intelligence called a Diffusion model -Similar to the ones behind image generators like DALL-E – but it is adapted to work with 3D crystal structures. It gradually refines random arrangements of atoms into stable, useful materials that meet specific criteria.

The results go beyond previous methods. According to the paper, the materials produced by MatterGen are “more than twice as likely to be new and stable, and more than 15 times closer to the local energy minimum” than previous AI methods. This means that the materials created are more likely to be useful and can be physically created.

In one amazing demonstration, the team collaborated with scientists in China Shenzhen Institutes of Advanced Technology To collect new material, TaCr2O6designed by MatterGen. The real-world materials closely matched the AI’s predictions, validating the system’s practical utility.

Real-world applications could transform energy storage and computing

The system is particularly distinguished by its flexibility. They can be “fine-tuned” to produce materials with specific properties, from specific crystal structures to desired electronic or magnetic properties. This can be invaluable for designing materials for specific industrial applications.

The repercussions could be far-reaching. New materials are essential for developing 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 more cost-effective.

“From an industrial perspective, the potential here is huge,” Xie explained. “Human civilization has always relied on material innovations. If we can use generative AI to make materials design more efficient, it could accelerate progress in industries like energy, healthcare and others.”

Microsoft’s open source strategy aims to accelerate scientific discoveries

Microsoft has released MatterGen source code It is under an open source license, allowing researchers around the world to build on this technology. This step could accelerate the system’s impact in various scientific fields.

MatterGen’s development is part of the broader Microsoft scope Artificial intelligence for science An initiative aimed at accelerating scientific discovery using artificial intelligence. The project integrates with Microsoft Azure Quantum Elements platformThis may make the technology more accessible to companies and researchers through cloud computing services.

However, experts caution that although MatterGen represents a major advance, the path from computationally designed materials to practical applications still requires extensive testing and optimization. Although the system’s predictions are promising, they need experimental validation before being deployed in industry.

However, this technology represents an important step forward in using artificial intelligence to accelerate scientific discoveries. As Daniel Zoegner, one of the project’s lead researchers, noted: “We are deeply committed to research that can have a positive impact in the real world, and this is just the beginning.”