Scott Reed, Ph.D.
Abstract
Generative AI is rapidly moving beyond the chat window. For chemistry, its greatest potential may lie not in answering questions, but in supporting the knowledge work chemists already do: interpreting structures and spectra, reasoning through mechanisms, creating scientific figures, preparing instructional materials, searching chemical information, and coordinating complex workflows.
This talk explores how agents, harness engineering, and reusable skills can turn general-purpose language models into more capable chemistry tools. Rather than relying on a single prompt, these systems combine models with chemical software, structured data, deterministic calculations, memory, validation, and specialized workflows. Examples from chemical research and education illustrate how this approach can improve reliability while expanding what generative AI can actually accomplish. The broader goal is to consider what happens when AI moves from simply talking about chemistry to actively participating in the intellectual and computational work of chemistry.
When: October 23, 2026
Where: North Classroom 1207
Time: 11:00 am - 12:00pm