Skip to main content
Conceptual art depicts an atomic nucleus and merging neutron stars, respectively, areas of study in ORNL-led projects called NUCLEI and ENAF within the Scientific Discovery through Advanced Computing, or SciDAC, program. Credit: Adam Malin/ORNL, U.S. Dept. of Energy

ORNL is leading two nuclear physics research projects within the Scientific Discovery through Advanced Computing, or SciDAC, program from the Department of Energy Office of Science.

Plutonium oxide is loaded onto a truck for shipping. Adam Parkison/ORNL, U.S. Dept. of Energy

In June, ORNL hit a milestone not seen in more than three decades: producing a production-quality amount of plutonium-238

Researchers used the open-source Community Earth System Model to simulate the effects that extreme climatic conditions have on processes like land carbon storage. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Researchers from Oak Ridge National Laboratory and Northeastern University modeled how extreme conditions in a changing climate affect the land’s ability to absorb atmospheric carbon — a key process for mitigating human-caused emissions. They found that 88% of Earth’s regions could become carbon emitters by the end of the 21st century. 

Tom Karnowski (left) and Jordan Johnson (right). Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Tom Karnowski and Jordan Johnson of ORNL have been named chair and vice chair, respectively, of the East Tennessee section of the Institute of Electrical and Electronics Engineers, or IEEE.

The DEMAND single crystal diffractometer at the High Flux Isotope Reactor, or HFIR, is the latest neutron instrument at the Department of Energy’s Oak Ridge National Laboratory to be equipped with machine learning-assisted software, called ReTIA. Credit: Jeremy Rumsey/ORNL, U.S. Dept. of Energy

Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.

A rendering of the CFM RISE program’s open fan architecture. (bottom) A GE visualization of turbulent flow in the tip region of an open fan blade using the Frontier supercomputer at ORNL. Credit: CFM, GE Research (CFM is a 50­–50 joint company between GE and Safran Aircraft Engines)

Outside the high-performance computing, or HPC, community, exascale may seem more like fodder for science fiction than a powerful tool for scientific research. Yet, when seen through the lens of real-world applications, exascale computing goes from ethereal concept to tangible reality with exceptional benefits.

A new nanoscience study led by an ORNL quantum researcher takes a big-picture look at how scientists study materials at the smallest scales. Credit: Getty Images

A new nanoscience study led by a researcher at ORNL takes a big-picture look at how scientists study materials at the smallest scales.

oxygen isotope 28

Rare isotope oxygen-28 has been determined to be "barely unbound" by experiments led by researchers at the Tokyo Institute of Technology and by computer simulations conducted at ORNL. The findings from this first-ever observation of 28O answer a longstanding question in nuclear physics: can you get bound isotopes in a very neutron-rich region of the nuclear chart, where instability and radioactivity are the norm? 

Madhavi Martin portrait image

Madhavi Martin brings a physicist’s tools and perspective to biological and environmental research at the Department of Energy’s Oak Ridge National Laboratory, supporting advances in bioenergy, soil carbon storage and environmental monitoring, and even helping solve a murder mystery.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine