Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
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.
Researchers used the world’s first exascale supercomputer to run one of the largest simulations of an alloy ever and achieve near-quantum accuracy.
The Exascale Small Modular Reactor effort, or ExaSMR, is a software stack developed over seven years under the Department of Energy’s Exascale Computing Project to produce the highest-resolution simulations of nuclear reactor systems to date. Now, ExaSMR has been nominated for a 2023 Gordon Bell Prize by the Association for Computing Machinery and is one of six finalists for the annual award, which honors outstanding achievements in high-performance computing from a variety of scientific domains.
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
Three researchers at ORNL have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.