Watch a distinguished lecture where Larry Zitnick, Research Director at Meta's Fundamental AI Research team, explores the intersection of artificial intelligence and atomic modeling. Discover how machine learning potentials and large training datasets are revolutionizing our understanding of atomic interactions, with practical applications in drug discovery and climate change mitigation. Learn about cutting-edge AI models and their real-world implementation, including a detailed case study on predicting catalyst performance for renewable fuel synthesis. Explore current challenges in generative AI models, scalability issues, and the importance of experimental validation. Drawing from his extensive experience in computer vision, FastMRI project leadership, and development of PhotoDNA technology, Zitnick provides valuable insights into the future of AI-driven atomic modeling and its potential impact on solving global challenges.