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Media Contacts
Joseph Chapman, a research scientist in quantum communications at ORNL, was given the Physical Review Applied Reviewer Excellence 2024 award for his work as a peer reviewer for the journal Physical Review Applied.
ORNL researchers have teamed up with other national labs to develop a free platform called Open Energy Data Initiative Solar Systems Integration Data and Modeling to better analyze the behavior of electric grids incorporating many solar projects.
ORNL scientists are working on a project to engineer and develop a cryogenic ion trap apparatus to simulate quantum spin liquids, a key research area in materials science and neutron scattering studies.
Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.
Groundbreaking report provides ambitious framework for accelerating clean energy deployment while minimizing risks and costs in the face of climate change.
Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions.
ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.
Researchers simulated a key quantum state at one of the largest scales reported, with support from the Quantum Computing User Program, or QCUP, at ORNL.
Held in Cocoa Beach, Florida from March 11 to 14, researchers across the computing and data spectra participated in sessions developed by staff members from the Department of Energy’s Oak Ridge National Laboratory, or ORNL, Sandia National Laboratories and the Swiss National Supercomputing Centre.
In the age of easy access to generative AI software, user can take steps to stay safe. Suhas Sreehari, an applied mathematician, identifies misconceptions of generative AI that could lead to unintentionally bad outcomes for a user.