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To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science.
A modeling analysis led by ORNL gives the first detailed look at how geothermal energy can relieve the electric power system and reduce carbon emissions if widely implemented across the United States within the next few decades.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
ORNL researchers have developed a novel way to encapsulate salt hydrate phase-change materials within polymer fibers through a coaxial pulling process. The discovery could lead to the widespread use of the low-carbon materials as a source of insulation for a building’s envelope.
ORNL and the University of Tennessee, Knoxville, co-hosted the 2023 National Society of Black Physicists Annual Conference with the theme "Frontiers in Physics: From Quantum to Materials to the Cosmos.” As part of the three-day conference held near UT, attendees took a 30-mile trip to the ORNL campus for facility tours, science talks and workshops.
Scientists at the Department of Energy’s Oak Ridge National Laboratory are using a new modeling framework in conjunction with data collected from marshes in the Mississippi Delta to improve predictions of climate-warming methane and nitrous oxide.
Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
Researchers at the Department of Energy’s Oak Ridge and Lawrence Berkeley National Laboratories are evolving graph neural networks to scale on the nation’s most powerful computational resources, a necessary step in tackling today’s data-centric
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.