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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Integrating AI-enabled analysis and agentic workflows at scientific user facilities
Author(s) Tanny Chavez Esparza, Xiaoya Chong, Bowen Zheng, Ka Hung Chan, Monika Choudhary, Wiebke Koepp, Dylan McReynolds, Alexander Hexemer
On-Site Speaker (Planned) Tanny Chavez Esparza
Abstract Scope Scientific user facilities generate large, heterogeneous datasets across diverse instruments, techniques, and research domains, creating opportunities to integrate artificial intelligence more directly into experimental workflows. At the Advanced Light Source, we are developing capabilities that connect experimental data collection, computational resources, metadata and provenance capture, automated analysis, and reproducible workflows across beamlines. In parallel, we are exploring agentic AI systems that assist with experimental planning and execution through predefined, constrained instrument capabilities. Together, these developments aim to reduce analysis bottlenecks, improve reproducibility, and enable safer, more scalable use of AI in experimental science. This work highlights practical considerations for deploying reusable AI capabilities across multidisciplinary user-facility environments.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Characterization, Computational Materials Science & Engineering

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