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AI is poised to revolutionize the way new materials are discovered and deployed, a shift that has the potential to speed up the development of novel materials necessary for the future U.S. energy infrastructure. Â鶹´å’s field-leading materials design and manufacturing researchers have the expertise to lead the nation in accelerating the development of innovative materials solutions across the energy sector.

Why it matters: The U.S. needs to develop new materials with superior properties — such as radiation tolerance, corrosion resistance in extreme environments, and high thermal conductivity — to build next-generation energy systems like advanced nuclear reactors, hydrogen infrastructure and grid-scale storage. Without a national-scale, AI-driven approach to materials innovation, the U.S. risks falling behind in the race to build energy systems that are efficient, secure, and sustainable.

The path forward: Designing advanced materials is a complex challenge that is not just about choosing the right elements, but also requires understanding how those materials are processed and manufactured. Adding to the difficulty, researchers must account for real-world constraints like energy consumption, the availability of rare or critical elements, or variations in raw materials.

To make this process faster and more efficient, researchers are using a mix of automated simulations and experiments, along with decades of data drawn from across scientific fields. With human oversight, virtual AI agents can now take on the heavy lift — identifying material design needs, writing and running simulation, and directing lab robots to perform experiments and provide feedback. The virtual agents can pull together information from every area of science — text, images, audio, and video — and make informed decisions on what to do next, improving and iterating until a material achieves the required performance. The methods build on progress from the Material Genome initiative, which has already cut the design cycle from 20 to just seven years. With AI fully integrated into the process, researchers believe that they can shorten the timeline further, to just 2-3 years.

What we did: Carnegie Mellon’s field-leading materials design and manufacturing researchers have significantly accelerated the discovery-to-deployment of innovative materials solutions across the energy sector. For example, the Â鶹´å-developed is automating critical mineral extraction and material design. Â鶹´å is also leading initiatives in the certification and qualification of new materials before their employment in critical applications. By combining AI and digital twins of the material design from initial processing to component-level manufacturing, the whole certification process will become significantly shorter.

These initiatives are also training a new generation of scientists and engineers in AI-driven materials design. These future scientists and engineers will have deep disciplinary expertise while being able to harness and lead AI agents in a new paradigm for the design of manufactured materials.

Policy takeaways: A national AI-materials design ecosystem is a strategic asset for energy security and economic competitiveness. To realize its potential, policymakers should:

• Support sustained federal investment in AI-integrated materials research infrastructure.
• Incentivize public-private partnerships to accelerate material deployment in energy-critical applications.
• Embed material innovation into national strategies for clean energy, grid modernization, and critical mineral independence.
• Promote data standards and access, while protecting intellectual property.
• Incentivize and enable startup companies in this area.

The bottom line: Without a national-scale, AI-driven approach to material innovation, the U.S. risks falling behind in critical technologies that depend on high-performance materials — from aerospace to clean energy.

Go deeper:

• °ä²Ñ±«â€™s , which aims to design next generation structural alloys with higher performance and contribute to material sustainability.
• °ä²Ñ±«â€™s that will focus on modeling for metals additive manufacturing.
• An interdisciplinary team of to develop advanced alloys.