Self-driving Multimodal Studies at User Facilities

Published

January 22, 2023

Author(s)

Bruce D. Ravel, Phillip Michael Maffettone, Daniel Allan, Stuart Campbell, Matthew Carbone, Brian DeCost, Howie Joress, Dmitri Gavrilov, Marcus Hanwell, Joshua Lynch, Stuart Wilkins, Jakub Wlodek, Daniel Olds

Abstract

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.

Citation

arXiv

Pub Weblink

https://arxiv.org/abs/2301.09177

Pub Type

Websites

Download Paper

https://doi.org/10.48550/arXiv.2301.09177

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Keywords

Machine learning, user facilities

Citation

Ravel, B. , Maffettone, P. , Allan, D. , Campbell, S. , Carbone, M. , DeCost, B. , Joress, H. , Gavrilov, D. , Hanwell, M. , Lynch, J. , Wilkins, S. , Wlodek, J. and Olds, D. (2023), Self-driving Multimodal Studies at User Facilities, arXiv, [online], https://doi.org/10.48550/arXiv.2301.09177, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936039, https://arxiv.org/abs/2301.09177 (Accessed October 20, 2025)