AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

Technical Report · Sat Feb 01 04:00:00 EST 2020

DOI:https://doi.org/10.2172/1604756· OSTI ID:1604756

Stevens, Rick ^[1]; Taylor, Valerie ^[1]; Nichols, Jeff ^[2]; Maccabe, Arthur Barney ^[2]; Yelick, Katherine ^[3]; Brown, David ^[3]

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1. Argonne National Lab. (ANL), Argonne, IL (United States)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE; Lawrence Berkeley National Laboratory (LBNL); Argonne National Laboratory (ANL); Oak Ridge National Laboratory (ORNL)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1604756
• Report Number(s): ANL--20/17; 158802
• Country of Publication: United States
• Language: English

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