In this episode of the IEEE AI Podcast, host Daswin De Silva explores the groundbreaking efforts at the University of Missouri to revolutionize materials science through artificial intelligence and machine learning. Guests Matthias Young, an expert in thin-film coatings and electrochemistry, and Jim Keller, a pioneer in computational intelligence, discuss an interdisciplinary project aimed at moving beyond traditional, serendipitous discovery methods. By leveraging Large Language Models (LLMs) and Bayesian optimization algorithms, the team can navigate vast parameter spaces to model and test material “recipes”—such as sustainable battery electrodes and advanced semiconductors—in a fraction of the time required by manual lab experiments. This conversation highlights the unique partnership between researchers from different fields and backgrounds, and explores how autonomous material discovery is poised to transform the manufacturing landscape, enhance energy efficiency, and address global resource scarcity.