Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

Junqi Yin; Guannan Zhang; Huibo Cao; Sajal Dash; Bryan C. Chakoumakos; Feiyi Wang

doi:10.1007/978-3-031-23606-8_15

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

Junqi Yin, Guannan Zhang, Huibo Cao, Sajal Dash, Bryan C. Chakoumakos, Feiyi Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.

Original language	English
Title of host publication	Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers
Editors	Kothe Doug, Geist Al, Swaroop Pophale, Hong Liu, Suzanne Parete-Koon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	244-256
Number of pages	13
ISBN (Print)	9783031236051
DOIs	https://doi.org/10.1007/978-3-031-23606-8_15
State	Published - 2022
Event	Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022 - Virtual, Online Duration: Aug 24 2022 → Aug 25 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1690 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022
City	Virtual, Online
Period	08/24/22 → 08/25/22

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. This neutron data used resources at the High Flux Isotope Reactor, the DOE Office of Science User Facility operated by ORNL. Keywords: Autonomous workflow · Inference at the edge · Integrated ecosystem · Image segmentation This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan).

Funders	Funder number
U.S. Department of Energy
Office of Science
Oak Ridge National Laboratory

Keywords

Autonomous workflow
Image segmentation
Inference at the edge
Integrated ecosystem

Access to Document

10.1007/978-3-031-23606-8_15

Cite this

Yin, J., Zhang, G., Cao, H., Dash, S., Chakoumakos, B. C., & Wang, F. (2022). Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. In K. Doug, G. Al, S. Pophale, H. Liu, & S. Parete-Koon (Eds.), Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers (pp. 244-256). (Communications in Computer and Information Science; Vol. 1690 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23606-8_15

Yin, Junqi ; Zhang, Guannan ; Cao, Huibo et al. / Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. editor / Kothe Doug ; Geist Al ; Swaroop Pophale ; Hong Liu ; Suzanne Parete-Koon. Springer Science and Business Media Deutschland GmbH, 2022. pp. 244-256 (Communications in Computer and Information Science).

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abstract = "The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.",

keywords = "Autonomous workflow, Image segmentation, Inference at the edge, Integrated ecosystem",

author = "Junqi Yin and Guannan Zhang and Huibo Cao and Sajal Dash and Chakoumakos, {Bryan C.} and Feiyi Wang",

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Yin, J , Zhang, G , Cao, H, Dash, S, Chakoumakos, BC & Wang, F 2022, Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. in K Doug, G Al, S Pophale, H Liu & S Parete-Koon (eds), Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1690 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 244-256, Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Virtual, Online, 08/24/22. https://doi.org/10.1007/978-3-031-23606-8_15

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. / Yin, Junqi ; Zhang, Guannan ; Cao, Huibo et al.
Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. ed. / Kothe Doug; Geist Al; Swaroop Pophale; Hong Liu; Suzanne Parete-Koon. Springer Science and Business Media Deutschland GmbH, 2022. p. 244-256 (Communications in Computer and Information Science; Vol. 1690 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Yin J , Zhang G , Cao H, Dash S, Chakoumakos BC, Wang F. Toward an Autonomous Workflow for Single Crystal Neutron Diffraction. In Doug K, Al G, Pophale S, Liu H, Parete-Koon S, editors, Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation - 22nd Smoky Mountains Computational Sciences and Engineering Conference, SMC 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 244-256. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-23606-8_15