Skip to main content
3D printed “Frankenstein design” collimator show the “scars” where the individual parts are joined

Scientists at ORNL have developed 3D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments

A small droplet of water is suspended in midair via an electrostatic levitator that lifts charged particles using an electric field that counteracts gravity. Credit: Iowa State University/ORNL, U.S. Dept. of Energy

How do you get water to float in midair? With a WAND2, of course. But it’s hardly magic. In fact, it’s a scientific device used by scientists to study matter.

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.

The DuAlumin-3D research team developed a lightweight, aluminum alloy for additive manufacturing. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy

Dean Pierce of ORNL and a research team led by ORNL’s Alex Plotkowski were honored by DOE’s Vehicle Technologies Office for development of novel high-performance alloys that can withstand extreme environments.

Tomonori Saito, Oak Ridge National Laboratory’s Inventor of the Year, was honored at Battelle’s Celebration of Solvers. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Tomonori Saito, a distinguished innovator in the field of polymer science and senior R&D staff member at ORNL, was honored on May 11 in Columbus, Ohio, at Battelle’s Celebration of Solvers.

Jeff Foster, Distinguished Staff Fellow at Oak Ridge National Laboratory, is looking for ways to control polymer sequencing for a variety of uses. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Chemist Jeff Foster is looking for ways to control sequencing in polymers that could result in designer molecules to benefit a variety of industries, including medicine and energy.

From left are UWindsor students Isabelle Dib, Dominik Dziura, Stuart Castillo and Maksymilian Dziura at ORNL’s Neutron Spin Echo spectrometer. Their work advances studies on a natural cancer treatment. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

A scientific instrument at ORNL could help create a noninvasive cancer treatment derived from a common tropical plant.

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.

Oak Ridge National Laboratory researchers used an invertible neural network, a type of artificial intelligence that mimics the human brain, to select the most suitable materials for desired properties, such as flexibility or heat resistance, with high chemical accuracy. The study could lead to more customizable materials design for industry.

A study led by researchers at ORNL could help make materials design as customizable as point-and-click.

Using quantum Monte Carlo methods, the researchers simulated bulk VO2. Yellow and turquoise represent changes in electron density between the excited and ground states of a compound composed of oxygen, in red, and vanadium, in blue, which allowed them to evaluate how an oxygen vacancy, in white, can alter the compound’s properties. Credit: Panchapakesan Ganesh/ORNL, U.S. Dept. of Energy

Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant