NSF AI grant $6M: the "AI Materials Institute (AI-MI)" discovers new materials with AI (Cornell)
The NSF awarded about $6M to the "AI Materials Institute (AI-MI)," which advances foundational AI research while accelerating the discovery of next-generation materials essential for sustainable energy, electronics, the environment, and quantum technologies. A science-ready large language model coupled with multimodal data aims to cut discovery cycles from months to days.
Grant overview (primary data)
- Award amount$6,000,000 / Est. total $20,000,000
- RecipientCornell University（NY）
- ProgramAI Research Institutes, NSF-Intel Semiconductr Partnrs
- Period2025-10-01 〜 2030-09-30
- FunderU.S. National Science Foundation (NSF) / NSF
Key points
- Advances foundational AI while accelerating next-gen materials (energy, electronics, environment, quantum)
- Integrates data generation, AI inference, and experiments to cut discovery cycles from months to days
- Builds AIMS-EC: a science-ready LLM + multimodal data cloud platform with natural-language queries
- Applications: qubit-ready moiré structures, superconductors, soft materials, microplastic-removal peptides
- About $6M, led by Cornell, NSF AI Research Institutes (Intel partnership), 2025–2030
The U.S. National Science Foundation (NSF) awarded about $6,000,000 to the NSF AI Materials Institute (AI-MI), led by Cornell University (NSF Award 2433348; program: AI Research Institutes / NSF-Intel semiconductor partnership; October 2025 – September 2030).
Per the abstract, the need for materials with improved or new properties is at the heart of many societal challenges, yet knowledge- and data-centric obstacles prevent prediction-driven materials discovery despite expanding experimental capabilities and data. AI-MI aims to propel foundational AI past the limits of existing algorithms via materials discovery, while accelerating the discovery of next-generation materials essential for sustainable energy, electronics, environmental stewardship, and quantum technologies. By tightly integrating data generation, AI inference, and rapid experimental feedback, it seeks to reduce discovery cycles from months to days and to establish reproducible, reusable workflows for the broader community.
At its core is AIMS-EC (the AI Materials Science Ecosystem), an open, cloud-based portal that couples a science-ready large language model with multimodal data streams (experimental measurements, simulations, images, and literature). Researchers can pose natural-language queries and receive transparent, data-grounded answers, unifying prediction, explanation, and experimental design in a single interface. Targeted applications include discovering two-dimensional moiré structures suitable for robust qubits, learning descriptors to guide new superconductors, finding functional soft materials for sustainability, and identifying peptides for microplastic removal, plus accelerating synthesis through self-driving labs. AI-MI also runs a comprehensive education program across all levels and engages industry collaborators.
Why it matters
A case of public funding to make AI a "discovery engine" for materials science. A useful read on the direction of U.S. investment for those tracking AI for Science and semiconductor, quantum, and energy-materials research.
FAQ
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Sources (primary)
Source: NSF Award Search (U.S. National Science Foundation, public domain). Amounts are the obligated amount. For privacy, we do not handle principal investigator names.
- NSF Award (original, official)
- NSF Award ID: 2433348