Mathematics Colloquia and Seminars

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Breakthroughs in materials science are essential for driving technological advances and addressing emerging grand challenges in energy and information. However, the discovery and design of novel materials remain hindered by a vast design space and bottlenecks in computational and experimental capabilities. In this talk, I will present how Artificial Intelligence (AI) can enable materials design and discovery by transforming current experimental characterization. Specifically, I will discuss three main areas that I have been advancing: (1) AI-driven spectroscopy prediction toward application-targeted materials discovery, (2) AI-enabled solutions to challenging inverse problems in materials characterization for enhanced knowledge extraction from experiments, and (3) On-the-fly experimental planning and materials properties learning using AI to improve experimental efficiency. This talk will feature studies spanning a broad range of scientific fields and AI methods, highlighting the versatile and transformative role of AI in driving scientific discoveries and accelerating materials research.