skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Outlook for Artificial Intelligence and Machine Learning at the NSLS-II

Abstract

We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimising the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.

Authors:
ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [1]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States). National Synchrotron Light Source II (NSLS-II)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1677662
Report Number(s):
BNL-219954-2020-JAAM
Journal ID: ISSN 2632-2153
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Machine Learning: Science and Technology
Additional Journal Information:
Journal Volume: 2; Journal Issue: 1; Journal ID: ISSN 2632-2153
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Campbell, Stuart, Allan, Daniel B., Barbour, Andi, Olds, Daniel, Rakitin, Maksim, Smith, Reid, and Wilkins, Stuart B.. Outlook for Artificial Intelligence and Machine Learning at the NSLS-II. United States: N. p., 2021. Web. https://doi.org/10.1088/2632-2153/abbd4e.
Campbell, Stuart, Allan, Daniel B., Barbour, Andi, Olds, Daniel, Rakitin, Maksim, Smith, Reid, & Wilkins, Stuart B.. Outlook for Artificial Intelligence and Machine Learning at the NSLS-II. United States. https://doi.org/10.1088/2632-2153/abbd4e
Campbell, Stuart, Allan, Daniel B., Barbour, Andi, Olds, Daniel, Rakitin, Maksim, Smith, Reid, and Wilkins, Stuart B.. Wed . "Outlook for Artificial Intelligence and Machine Learning at the NSLS-II". United States. https://doi.org/10.1088/2632-2153/abbd4e. https://www.osti.gov/servlets/purl/1677662.
@article{osti_1677662,
title = {Outlook for Artificial Intelligence and Machine Learning at the NSLS-II},
author = {Campbell, Stuart and Allan, Daniel B. and Barbour, Andi and Olds, Daniel and Rakitin, Maksim and Smith, Reid and Wilkins, Stuart B.},
abstractNote = {We describe the current and future plans for using artificial intelligence and machine learning (AI/ML) methods at the National Synchrotron Light Source II (NSLS-II), a scientific user facility at the Brookhaven National Laboratory. We discuss the opportunity for using the AI/ML tools and techniques developed in the data and computational science areas to greatly improve the scientific output of large scale experimental user facilities. We describe our current and future plans in areas including from detecting and recovering from faults, optimising the source and instrument configurations, streamlining the pipeline from measurement to insight, through data acquisition, processing, analysis. The overall strategy and direction of the NSLS-II facility in relation to AI/ML is presented.},
doi = {10.1088/2632-2153/abbd4e},
journal = {Machine Learning: Science and Technology},
number = 1,
volume = 2,
place = {United States},
year = {2021},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1 Figure 1: Aerial photograph of the National Synchrotron Light Source Ⅱ situated at Brookhaven National Laboratory. (Courtesy of Brookhaven National Laboratory)

Save / Share:

Works referenced in this record:

Bluesky's Ahead: A Multi-Facility Collaboration for an a la Carte Software Project for Data Acquisition and Management
journal, May 2019


Sirepo : an open-source cloud-based software interface for X-ray source and optics simulations
journal, October 2018

  • Rakitin, Maksim S.; Moeller, Paul; Nagler, Robert
  • Journal of Synchrotron Radiation, Vol. 25, Issue 6
  • DOI: 10.1107/S1600577518010986

ShadowOui : a new visual environment for X-ray optics and synchrotron beamline simulations
journal, October 2016


Introduction of the Sirepo-Bluesky interface and its application to the optimization problems
conference, September 2020

  • Rakitin, Maksim S.; Giles, Abigail; Swartz, Kaleb
  • Advances in Computational Methods for X-Ray Optics V
  • DOI: 10.1117/12.2569000

General method for automatic on-line beamline optimization based on genetic algorithm
journal, April 2015

  • Xi, Shibo; Borgna, Lucas Santiago; Du, Yonghua
  • Journal of Synchrotron Radiation, Vol. 22, Issue 3
  • DOI: 10.1107/S1600577515001861

AI-BL 1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm
journal, January 2017

  • Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong
  • Journal of Synchrotron Radiation, Vol. 24, Issue 1
  • DOI: 10.1107/S1600577516018117

Newton versus the machine: solving the chaotic three-body problem using deep neural networks
journal, April 2020

  • Breen, Philip G.; Foley, Christopher N.; Boekholt, Tjarda
  • Monthly Notices of the Royal Astronomical Society, Vol. 494, Issue 2
  • DOI: 10.1093/mnras/staa713

Simulation of experiments with partially coherent x-rays using Synchrotron Radiation Workshop
conference, September 2017

  • Chubar, Oleg; Rakitin, Maksim S.; Chen-Wiegart, Yu-Chen
  • Advances in Computational Methods for X-Ray Optics IV
  • DOI: 10.1117/12.2274481

Mastering the game of Go without human knowledge
journal, October 2017

  • Silver, David; Schrittwieser, Julian; Simonyan, Karen
  • Nature, Vol. 550, Issue 7676
  • DOI: 10.1038/nature24270

X-ray photon correlation spectroscopy
journal, August 2014


X-ray Photon Correlation Spectroscopy Studies of Surfaces and Thin Films
journal, September 2014

  • Sinha, Sunil K.; Jiang, Zhang; Lurio, Laurence B.
  • Advanced Materials, Vol. 26, Issue 46
  • DOI: 10.1002/adma.201401094

Submillisecond X-ray photon correlation spectroscopy from a pixel array detector with fast dual gating and no readout dead-time
journal, April 2016

  • Zhang, Qingteng; Dufresne, Eric M.; Grybos, Pawel
  • Journal of Synchrotron Radiation, Vol. 23, Issue 3
  • DOI: 10.1107/S1600577516005166

Remarkable Stability of Charge Density Wave Order in La 1.875 Ba 0.125 CuO 4
journal, October 2016


Static charge-density-wave order in the superconducting state of La 2 x Ba x CuO 4
journal, June 2017


Double-slit photoelectron interference in strong-field ionization of the neon dimer
journal, January 2019


Orbital Domain Dynamics in Magnetite below the Verwey Transition
journal, October 2018


Observation of Heterodyne Mixing in Surface X-Ray Photon Correlation Spectroscopy Experiments
journal, August 2003


Dynamics and rheology under continuous shear flow studied by x-ray photon correlation spectroscopy
journal, March 2010


Echoes in x-ray speckles track nanometer-scale plastic events in colloidal gels under shear
journal, December 2014


Brownian and advective dynamics in microflow studied by coherent X-ray scattering experiments
journal, October 2016

  • Urbani, Raphael; Westermeier, Fabian; Banusch, Benjamin
  • Journal of Synchrotron Radiation, Vol. 23, Issue 6
  • DOI: 10.1107/S1600577516012613

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013

  • Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
  • APL Materials, Vol. 1, Issue 1
  • DOI: 10.1063/1.4812323

Combinatorial appraisal of transition states for in situ pair distribution function analysis
journal, November 2017

  • Olds, Daniel; Peterson, Peter F.; Crawford, Michael K.
  • Journal of Applied Crystallography, Vol. 50, Issue 6
  • DOI: 10.1107/S1600576717015163

Advances in utilizing event based data structures for neutron scattering experiments
journal, September 2018

  • Peterson, Peter F.; Olds, Daniel; Savici, Andrei T.
  • Review of Scientific Instruments, Vol. 89, Issue 9
  • DOI: 10.1063/1.5034782

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.