| The Advanced Photon Source (APS) at Argonne organizes an ongoing seminar series, the APS Scientific Computation Seminar Series, focused on various aspects of computation for synchrotron science; talks are related to data analysis, reconstruction, theory, simulation, optimization, machine learning, automation, and visualization. The seminar provides an opportunity to present and learn about state-of-the-art software and the application of computational, AI/ML and mathematical techniques to synchrotron science. |
| Title: | Operating scientific facilities with foundation models and teachable agents | | Presenter(s): | Aikaterini Vriza, Materials/Ceramics/Metallurgical 2, NST
| | Date: | July 14, 2025 | | Time: | 1:00 PM (Central Daylight Savings Time) | | Location: | Join ZoomGov Meeting
https://argonne.zoomgov.com/j/1601444470?pwd=N1phbHZVdCtmcVR5cGh0c1Zhc0…
Meeting ID: 160 144 4470
Passcode: 937918
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Meeting ID: 160 144 4470
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| | Abstract: | Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Foundation Models, as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities. | | Bio: | Katerina is working towards the development of autonomous laboratories for chemistry and materials. Her research combines cheminformatics, multimodal data integration and AI-driven experimental design. She holds a PhD in Chemistry from the University of Liverpool and has worked as a postdoctoral researcher and as staff scientist at the Center of Nanoscale Materials in Argonne National Laboratory. Katerina is passionate about building tools that bridge experimental and computational research in materials science and recently in exploring the capabilities of AI Agents and Large Language models for operating large scientific facilities. |
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