Skip to main content
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.

Chathuddasie Amarasinghe explains her research poster, “Using Microfluidic Mother Machine Devices to Study the Correlated Dynamics of Ribosomes and Chromosomes in Escherichia Coli.” Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Speakers, scientific workshops, speed networking, a student poster showcase and more energized the Annual User Meeting of the Department of Energy’s Center for Nanophase Materials Sciences, or CNMS, Aug. 7-10, near Market Square in downtown Knoxville, Tennessee.

Oak Ridge National Laboratory entrance sign

The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.

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 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

The OpeN-AM experimental platform, installed at the VULCAN instrument, features a robotic arm that prints layers of molten metal to create complex shapes. Credit: Jill Hemman/ORNL, U.S Dept. of Energy

Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.