AI-Materials Institute (AI-MI)

The need for materials with improved or new properties is often at the heart of the challenges society faces. Despite massive expansion in experimental capabilities and data, knowledge- and data-centric challenges prevent prediction-driven materials discovery. The NSF AI Materials Institute (AI-MI) aims to propel foundational AI research past the limitations of existing AI algorithms by pursuing materials discovery and conquering knowledge- and data-centric challenges. AI-MI will advance the foundations of artificial intelligence while accelerating the discovery of next-generation materials essential for sustainable energy, electronics, environmental stewardship, and quantum technologies. AI-MI brings together computer scientists, materials researchers, and data scientists to leverage advances in AI technology and materials data to drive use-inspired progress in fundamental AI, catalyzing prediction-driven materials discoveries. By tightly integrating data generation, AI inference, and rapid experimental feedback, AI-MI aims to reduce discovery cycles from months to days and to establish reproducible, reusable workflows for the broader community. AI-MI plans to create the AI Materials Science Ecosystem (AIMS-EC)- an open, cloud-based portal. AIMS-EC will couple a science-ready large-language model with multimodal data streams (experimental measurements, simulations, images, and textual literature). This platform will allow researchers to pose natural-language queries and receive transparent, data-grounded answers, thereby unifying prediction, explanation, and experimental design in a single interface. AI-MI will apply the capabilities of AIMS-EC to discover two-dimensional moire structures with properties suitable for robust qubits, learn descriptors that can guide the design of new superconductors, discover new functional soft materials and mixtures for sustainability, and identify functional peptides for the removal of microplastics. Moreover, AI-MI will accelerate material synthesis through data-driven optimization of film growth and self-driving labs. AI-MI will implement a comprehensive educational program that covers AI and materials science across all levels of instruction. Through educational modules, internship programs, and partnerships, AI-MI will develop an agile talent pipeline to propel advancements at the intersection of AI and materials science for the next generation workforce, ultimately driving advancements nationwide. Moreover, AI-MI will engage with non-academic collaborators, especially in the materials industry, through training programs and internship placements, in addition to research collaborations on problems of practical importance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.