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Join Professor Sergei Kalinin from the University of Tennessee in this 47-minute Goldsmiths' seminar exploring how artificial intelligence and machine learning are revolutionizing materials science discovery. Learn how ML-driven electron and scanning probe microscopies can uncover structure-property relationships in complex materials, extract fundamental physical laws, and manipulate matter at nanometer and atomic scales. Discover the concept of probabilistic reward functions enabling autonomous research workflows while maintaining human-in-the-loop decision-making. Explore how these technologies can close the materials discovery loop by orchestrating diverse characterization tools and navigating between experiments and theoretical models. The seminar particularly focuses on operationalized materials and physics discovery in combinatorial libraries, where ML-enabled scanning probe microscopes autonomously perform topography, spectroscopy tuning, and space exploration. Understand strategies for extending these approaches to electron microscopy while bypassing sample preparation bottlenecks. This presentation lays the foundation for the automated laboratory of the future, where human intuition and AI-driven autonomy work together to accelerate materials discovery at unprecedented scales. The seminar was held on May 15, 2025, by Cambridge Materials.
