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Title: BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning

Abstract

To identify specific Priority Research Opportunities (PROs) for artificial intelligence and machine learning (AI/ML) at the US Department of Energy’s scientific user facilities, the Office of Basic Energy Sciences (BES) convened a roundtable of facility experts encompassing the fields of physics, chemistry, materials synthesis science, computational science, detector and accelerator technology, theory, modeling, simulation, and atomic-scale characterization techniques. The roundtable met on October 22–23, 2019, to identify coordinated, long-term AI/ML research efforts that will drive major advances in neutron, photon, and nanoscale sciences. This report describes the four PROs identified at the roundtable: PRO 1 on how AI/ML can extract high-value information from the large datasets; PRO 2 on how AI/ML can use such information in real time to maximize the facilities’ scientific output; PRO 3 on using AI/ML virtual laboratories (i.e., computational models of experimental facilities) to aid the facilities and user community in design and control of machine parameters and the design and execution of experiments, including training AI/ML models for PROs 1 and 2; and PRO 4 on how a shared scientific data infrastructure can provide tools to assemble and analyze the totality of data coming from user facilities. A section of the end of the report providesmore » a summary on computer science and mathematics that highlights where enhanced AI/ML capabilities could be particularly impactful for BES user facilities.« less

Authors:
 [1];  [2];  [3];  [2];  [1];  [2];  [4];  [2];  [5];  [4];  [3];  [1];  [2];  [4];  [6];  [1];  [2];  [5];  [4];  [7] more »;  [5];  [1];  [5];  [4];  [2];  [3];  [3] « less
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Brookhaven National Lab. (BNL), Upton, NY (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  5. Argonne National Lab. (ANL), Argonne, IL (United States)
  6. National Institute of Standards and Technology
  7. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
DOESC Office of Basic Energy Sciences
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1630823
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; 43 PARTICLE ACCELERATORS; 47 OTHER INSTRUMENTATION; 36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Ratner, Daniel, Sumpter, Bobby, Alexander, Frank, Billings, Jay Jay, Coffee, Ryan, Cousineau, Sarah, Denes, Peter, Doucet, Mathieu, Foster, Ian, Hexemer, Alex, Hidas, Dean, Huang, Xiaobiao, Kalinin, Sergei, Kiran, Mariam, Kusne, A. Gilad, Mehta, Apurva, Ramirez-Cuesta, Anibal, Sankaranarayanan, Subramanian, Scott, Mary, Stevens, Mark, Sun, Yipeng, Thayer, Jana, Toby, Brian, Ushizima, Daniela, Vasudevan, Rama, Wilkins, Stuart, and Yager, Kevin. BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning. United States: N. p., 2019. Web. doi:10.2172/1630823.
Ratner, Daniel, Sumpter, Bobby, Alexander, Frank, Billings, Jay Jay, Coffee, Ryan, Cousineau, Sarah, Denes, Peter, Doucet, Mathieu, Foster, Ian, Hexemer, Alex, Hidas, Dean, Huang, Xiaobiao, Kalinin, Sergei, Kiran, Mariam, Kusne, A. Gilad, Mehta, Apurva, Ramirez-Cuesta, Anibal, Sankaranarayanan, Subramanian, Scott, Mary, Stevens, Mark, Sun, Yipeng, Thayer, Jana, Toby, Brian, Ushizima, Daniela, Vasudevan, Rama, Wilkins, Stuart, & Yager, Kevin. BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning. United States. doi:10.2172/1630823.
Ratner, Daniel, Sumpter, Bobby, Alexander, Frank, Billings, Jay Jay, Coffee, Ryan, Cousineau, Sarah, Denes, Peter, Doucet, Mathieu, Foster, Ian, Hexemer, Alex, Hidas, Dean, Huang, Xiaobiao, Kalinin, Sergei, Kiran, Mariam, Kusne, A. Gilad, Mehta, Apurva, Ramirez-Cuesta, Anibal, Sankaranarayanan, Subramanian, Scott, Mary, Stevens, Mark, Sun, Yipeng, Thayer, Jana, Toby, Brian, Ushizima, Daniela, Vasudevan, Rama, Wilkins, Stuart, and Yager, Kevin. Tue . "BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning". United States. doi:10.2172/1630823. https://www.osti.gov/servlets/purl/1630823.
@article{osti_1630823,
title = {BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning},
author = {Ratner, Daniel and Sumpter, Bobby and Alexander, Frank and Billings, Jay Jay and Coffee, Ryan and Cousineau, Sarah and Denes, Peter and Doucet, Mathieu and Foster, Ian and Hexemer, Alex and Hidas, Dean and Huang, Xiaobiao and Kalinin, Sergei and Kiran, Mariam and Kusne, A. Gilad and Mehta, Apurva and Ramirez-Cuesta, Anibal and Sankaranarayanan, Subramanian and Scott, Mary and Stevens, Mark and Sun, Yipeng and Thayer, Jana and Toby, Brian and Ushizima, Daniela and Vasudevan, Rama and Wilkins, Stuart and Yager, Kevin},
abstractNote = {To identify specific Priority Research Opportunities (PROs) for artificial intelligence and machine learning (AI/ML) at the US Department of Energy’s scientific user facilities, the Office of Basic Energy Sciences (BES) convened a roundtable of facility experts encompassing the fields of physics, chemistry, materials synthesis science, computational science, detector and accelerator technology, theory, modeling, simulation, and atomic-scale characterization techniques. The roundtable met on October 22–23, 2019, to identify coordinated, long-term AI/ML research efforts that will drive major advances in neutron, photon, and nanoscale sciences. This report describes the four PROs identified at the roundtable: PRO 1 on how AI/ML can extract high-value information from the large datasets; PRO 2 on how AI/ML can use such information in real time to maximize the facilities’ scientific output; PRO 3 on using AI/ML virtual laboratories (i.e., computational models of experimental facilities) to aid the facilities and user community in design and control of machine parameters and the design and execution of experiments, including training AI/ML models for PROs 1 and 2; and PRO 4 on how a shared scientific data infrastructure can provide tools to assemble and analyze the totality of data coming from user facilities. A section of the end of the report provides a summary on computer science and mathematics that highlights where enhanced AI/ML capabilities could be particularly impactful for BES user facilities.},
doi = {10.2172/1630823},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {10}
}