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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.22959 |
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| _version_ | 1866912622151991296 |
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| author | Corrao, Adam A. Maffettone, Phillip M. Ravel, Bruce Caswell, Thomas A. Campbell, Stuart I. Joress, Howie Wilkins, Stuart Olds, Daniel |
| author_facet | Corrao, Adam A. Maffettone, Phillip M. Ravel, Bruce Caswell, Thomas A. Campbell, Stuart I. Joress, Howie Wilkins, Stuart Olds, Daniel |
| contents | High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enabling real-time orchestration of multi-beamline, multi-modal experiments. AI agents perform on-the-fly reduction, clustering, Gaussian process modelling, and Bayesian optimization driven data acquisition, while users monitor agent behavior and visualize results live. Combinatorial libraries of the ternary Al-Ni-Pt system were spatially mapped by X-ray diffraction and X-ray absorption fine structure measurements at the PDF and BMM beamlines, respectively. Dynamic switching between AI-driven and conventional grid mapping strategies was achieved, demonstrating the flexible workflows possible through this framework. A digital twin constructed from a simulated Al-Li-Fe oxide dataset shows that AI-driven mapping strategies outperform conventional mapping as well as random sampling by prioritizing measurements that better resolve both phase boundaries and localized minority phases. This framework supports plug-and-play capabilities, and establishes a foundation for routine multi-modal, AI-assisted large-scale user-facility operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22959 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments Corrao, Adam A. Maffettone, Phillip M. Ravel, Bruce Caswell, Thomas A. Campbell, Stuart I. Joress, Howie Wilkins, Stuart Olds, Daniel Applied Physics Materials Science High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enabling real-time orchestration of multi-beamline, multi-modal experiments. AI agents perform on-the-fly reduction, clustering, Gaussian process modelling, and Bayesian optimization driven data acquisition, while users monitor agent behavior and visualize results live. Combinatorial libraries of the ternary Al-Ni-Pt system were spatially mapped by X-ray diffraction and X-ray absorption fine structure measurements at the PDF and BMM beamlines, respectively. Dynamic switching between AI-driven and conventional grid mapping strategies was achieved, demonstrating the flexible workflows possible through this framework. A digital twin constructed from a simulated Al-Li-Fe oxide dataset shows that AI-driven mapping strategies outperform conventional mapping as well as random sampling by prioritizing measurements that better resolve both phase boundaries and localized minority phases. This framework supports plug-and-play capabilities, and establishes a foundation for routine multi-modal, AI-assisted large-scale user-facility operations. |
| title | A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments |
| topic | Applied Physics Materials Science |
| url | https://arxiv.org/abs/2509.22959 |