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The annual Director's Awards recognized four individuals and teams including awards for leadership in quantum simulation development and application on high-performance computing platforms, and revolutionary advancements in the area of microbial
Paul J. Hanson, ORNL Corporate Fellow, has been elected to the 2020 Class of Fellows of the American Geophysical Union.
Researchers at Oak Ridge National Laboratory were part of an international team that collected a treasure trove of data measuring precipitation, air particles, cloud patterns and the exchange of energy between the atmosphere and the sea ice.
Pauling’s Rules is the standard model used to describe atomic arrangements in ordered materials. Neutron scattering experiments at Oak Ridge National Laboratory confirmed this approach can also be used to describe highly disordered materials.
Two scientists with the Department of Energy’s Oak Ridge National Laboratory have been elected fellows of the American Physical Society.
Geoffrey L. Greene, a professor at the University of Tennessee, Knoxville, who holds a joint appointment with ORNL, will be awarded the 2021 Tom Bonner Prize for Nuclear Physics from the American Physical Society.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
A UCLA-led team that discovered the first intrinsic ferromagnetic topological insulator – a quantum material that could revolutionize next-generation electronics – used neutrons at Oak Ridge National Laboratory to help verify their finding.
Combining expertise in physics, applied math and computing, Oak Ridge National Laboratory scientists are expanding the possibilities for simulating electromagnetic fields that underpin phenomena in materials design and telecommunications.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.