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red and green sphagnum moss

A type of peat moss has surprised scientists with its climate resilience: Sphagnum divinum is actively speciating in response to hot, dry conditions. 

Carinata, pictured in full bloom at a producer’s field in Georgia, is a winter cover crop of interest as a feedstock for sustainable aviation fuel. Credit: Southeast Partnership for Advanced Renewables from Carinata

Oak Ridge National Laboratory scientists led the development of a supply chain model revealing the optimal places to site farms, biorefineries, pipelines and other infrastructure for sustainable aviation fuel production.

Samantha Peters co-designed and conducted experiments using ORNL’s high-performance mass spectrometry techniques to prove that bacteriophages deploy genetic code-switching to overwhelm and destroy host bacteria. Credit: Genevieve Martin, ORNL/U.S. Dept. of Energy

Scientists at ORNL have confirmed that bacteria-killing viruses called bacteriophages deploy a sneaky tactic when targeting their hosts: They use a standard genetic code when invading bacteria, then switch to an alternate code at later stages of

ORNL’s Tomás Rush explores the secret lives of fungi and plants for insights into the interactions that determine plant health. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Tomás Rush began studying the mysteries of fungi in fifth grade and spent his college intern days tromping through forests, swamps and agricultural lands searching for signs of fungal plant pathogens causing disease on host plants.

Shown here is the structure of the NEMO protein. A team from ORNL conducted extensive molecular dynamics work on Summit by using both quantum mechanics and machine-learning methods to look at the binding affinity of NEMO and 3CLpro in humans and other species and to consider the structural models derived from the sequences of other coronaviruses. Image courtesy Nature Communications, Dan Jacobson/ORNL.

A new paper published in Nature Communications adds further evidence to the bradykinin storm theory of COVID-19’s viral pathogenesis — a theory that was posited two years ago by a team of researchers at the Department of Energy’s Oak Ridge National Laboratory.

Data from different sources are joined on platforms created by ORNL researchers to offer better information for decision makers. Credit: ORNL/Nathan Armistead

When the COVID-19 pandemic stunned the world in 2020, researchers at ORNL wondered how they could extend their support and help

LandScan Global depicts population distribution estimates across the planet. The darker orange and red colors above indicate higher population density. Credit: ORNL, U.S. Dept. of Energy

It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.

The ORNL researchers’ findings may enable better detection of uranium tetrafluoride hydrate, a little-studied byproduct of the nuclear fuel cycle, and better understanding of how environmental conditions influence the chemical behavior of fuel cycle materials. Credit: Kevin Pastoor/Colorado School of Mines

ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.

ORNL, VA and Harvard researchers developed a sparse matrix full of anonymized information on what is thought to be the largest cohort of healthcare data used for this type of research in the U.S. The matrix can be probed with different methods, such as KESER, to gain new insights into human health. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

A team of researchers has developed a novel, machine learning–based  technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography.  Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.