NSF and Intel Invest $20M in Cornell AI Materials Institute

The U.S. National Science Foundation, in partnership with Intel, will invest $20 million over five years to establish the Artificial Intelligence Materials Institute (NSF AI-MI) at Cornell University, announced on July 29. Directed by Eun-Ah Kim, the institute aims to accelerate materials discovery for applications including sustainable energy and quantum technologies by integrating artificial intelligence methods with human scientific expertise. Researchers from Cornell, Princeton University, the City College of the City University of New York, the Advanced Science Research Center at CUNY, and Boston University will collaborate, utilising data generated from facilities such as the Cornell High Energy Synchrotron Source (CHESS) to enable AI-driven materials prediction and enhance fundamental understanding of both AI and materials science. The initiative represents a shift from serendipitous materials discovery towards targeted design, leveraging machine learning to analyse extensive research datasets.

The newly established Artificial Intelligence Materials Institute (NSF AI-MI), funded by a $20 million, five-year investment from the U.S. National Science Foundation and Intel, will focus on integrating artificial intelligence with materials science to accelerate materials discovery. Traditionally, materials research has often relied on chance encounters; however, the institute aims to transition towards the intentional design and synthesis of novel materials. This shift is enabled by the increasing availability of substantial datasets generated during materials research, including real-time data from facilities such as the Cornell High Energy Synchrotron Source (CHESS).

The institute’s core objective is to leverage AI’s analytical capabilities to predict material properties and guide development, moving beyond serendipitous discovery. A key component of the NSF AI-MI’s research agenda is the development of “trustworthy AI” alongside fundamental advancements in AI understanding. This dual focus acknowledges the necessity of reliable and interpretable AI systems within materials science, ensuring predictive accuracy and scientific validity. The institute’s work in this area will be crucial as the field increasingly relies on AI materials discovery to accelerate innovation in sustainable energy, advanced electronics, environmental stewardship, and quantum technologies.