Skip to main content
Sarah Walters portrait

Walters is working with a team of geographers, linguists, economists, data scientists and software engineers to apply cultural knowledge and patterns to open-source data in an effort to document and report patterns of human movement through previously unstudied spaces.

: This schematic of tokamak core-pedestal-boundary regions show what will be simulated by an ORNL project applying machine learning to plasma physics modeling. Credit: Giacomin et al., J. Comput. Phys., 463, (2022) 111294, https://doi.org/10.1016/j.jcp.2022.11294

ORNL will lead three new DOE-funded projects designed to bring fusion energy to the grid on a rapid timescale.

Photo collage with text that reads " A New era of discovery"

ORNL, a bastion of nuclear physics research for the past 80 years, is poised to strengthen its programs and service to the United States over the next decade if national recommendations of the Nuclear Science Advisory Committee, or NSAC, are enacted.

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

Attendees of SMC23 pose for their annual group photo in downtown Knoxville, TN.

ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.

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.

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.

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