[Office of Basic Energy Sciences (BES)] Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning

Technical Report · Tue Oct 22 00:00:00 EDT 2019

DOI:https://doi.org/10.2172/1630823· OSTI ID:1630823

Ratner, Daniel ^[1]; Sumpter, Bobby ^[2]; Alexander, Frank ^[3]; Billings, Jay Jay ^[2]; Coffee, Ryan ^[1]; Cousineau, Sarah ^[2]; Denes, Peter ^[4]; Doucet, Mathieu ^[2]; Foster, Ian ^[5]; Hexemer, Alex ^[4]; Hidas, Dean ^[3]; Huang, Xiaobiao ^[1]; Kalinin, Sergei ^[2]; Kiran, Mariam ^[4]; Kusne, A. Gilad ^[6]; Mehta, Apurva ^[1]; Ramirez-Cuesta, Anibal (Timmy) ^[2]; Sankaranarayanan, Subramanian ^[5]; Scott, Mary ^[4]; Stevens, Mark ^[7] ; Sun, Yipeng ^[5]; Thayer, Jana ^[1]; Toby, Brian ^[5]; Ushizima, Daniela ^[4]; Vasudevan, Rama ^[2]; Wilkins, Stuart ^[3]; Yager, Kevin ^[3]

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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)

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.

• Research Organization: DOESC Office of Basic Energy Sciences
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• OSTI ID: 1630823
• Report Number(s): DOE/BES-1630823
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
96 KNOWLEDGE MANAGEMENT AND PRESERVATION