Skip to main content
Sergei Kalinin

Five researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.

An organic solvent and water separate and form nanoclusters on the hydrophobic and hydrophilic sections of plant material, driving the efficient deconstruction of biomass. Credit: Michelle Lehman/ORNL, U.S. Dept. of Energy

Scientists at ORNL used neutron scattering and supercomputing to better understand how an organic solvent and water work together to break down plant biomass, creating a pathway to significantly improve the production of renewable

Members of the international team simulated changes to the start times of monsoon seasons across the globe, with warm colors representing onset delays. Credit: Moetasim Ashfaq and Adam Malin/Oak Ridge National Laboratory, U.S. Dept. of Energy

Scientists from the Department of Energy’s Oak Ridge National Laboratory and a dozen other international research institutions have produced the most elaborate set of projections to date that illustrates possible futures for major monsoon regions.

The protease protein is both shaped like a heart and functions as one, allowing the virus replicate and spread. Inhibiting the protease would block virus reproduction. Credit: Andrey Kovalevsky/ORNL, U.S. Dept. of Energy

A team of researchers has performed the first room-temperature X-ray measurements on the SARS-CoV-2 main protease — the enzyme that enables the virus to reproduce.

Before the demonstration, the team prepared QKD equipment (pictured) at ORNL. Image credit: Genevieve Martin/Oak Ridge National Laboratory, U.S. Dept. of Energy

For the second year in a row, a team from the Department of Energy’s Oak Ridge and Los Alamos national laboratories led a demonstration hosted by EPB, a community-based utility and telecommunications company serving Chattanooga, Tennessee.

Transformational Challenge Reactor Demonstration items

Researchers at the Department of Energy’s Oak Ridge National Laboratory are refining their design of a 3D-printed nuclear reactor core, scaling up the additive manufacturing process necessary to build it, and developing methods

Simulations forecast nationwide increase in human exposure to extreme climate events

OAK RIDGE, Tenn., May 5, 2020 — By 2050, the United States will likely be exposed to a larger number of extreme climate events, including more frequent heat waves, longer droughts and more intense floods, which can lead to greater risks for human health, ecosystem stability and regional economies.

Data scientists at Oak Ridge National Laboratory have completed a study of long-term trends in the relationship between the timing of tree leafing and rising temperatures in the United States. The information is being incorporated into DOE’s Energy Exascale Earth System Model. Photo Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy

A team of scientists led by Oak Ridge National Laboratory found that while all regions of the country can expect an earlier start to the growing season as temperatures rise, the trend is likely to become more variable year-over-year in hotter regions.

Infected Poplar

Scientists studying a valuable, but vulnerable, species of poplar have identified the genetic mechanism responsible for the species’ inability to resist a pervasive and deadly disease. Their finding, published in the Proceedings of the National Academy of Sciences, could lead to more successful hybrid poplar varieties for increased biofuels and forestry production and protect native trees against infection.

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the