Non-technical abstract: This project will accelerate the discovery of new superconducting materials through a transformative approach that combines artificial intelligence (AI), quantum theory, and experimental synthesis. Superconductors are essential for technologies ranging from MRI systems and high-field magnets to quantum computing and sustainable energy. Yet analysis of known compounds suggests that only a small fraction of potential superconductors may have been discovered. This project aims to significantly expand the number of known superconductors and identify materials optimized for practical applications—specifically those with high critical temperatures and magnetic fields, ductility for wire fabrication, and three-dimensional electronic structures for enhanced performance. A core educational mission will train a group of students in AI-driven materials research and develop hands-on experiment kits for K–12 classrooms to promote STEM engagement. Partnerships with national laboratories, industry, and international collaborators will ensure timely and impactful transition of discoveries to real-world applications. Technical abstract: The research integrates two complementary AI methods to accelerate superconductor discovery. Property prediction models based on graph neural networks will estimate superconducting characteristics—such as electron-phonon spectral functions, critical temperatures, and critical fields—directly from crystal structures. In parallel, ge