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Illustration of a real-time simulation showing a metallic nanoparticle’s optical response to light using RT-TDDFT. The image depicts electron oscillations and surrounding electromagnetic fields. Four inset panels represent applications: plasmon-enhanced biosensing, quantum computing, photochemical catalysis, and cancer detection through photothermal therapy.

A research team from the Department of Energy’s Oak Ridge National Laboratory, in collaboration with North Carolina State University, has developed a simulation capable of predicting how tens of thousands of electrons move in materials in real time, or natural time rather than compute time.

Illustration of the GRETA detector, a spherical array of metal cylinders. The detector is divided into two halves to show the inside of the machine. Both halves are attached to metal harnesses, displayed against a black and green cyber-themed background.

Analyzing massive datasets from nuclear physics experiments can take hours or days to process, but researchers are working to radically reduce that time to mere seconds using special software being developed at the Department of Energy’s Lawrence Berkeley and Oak Ridge national laboratories.  

Animated graphic with a plant on the right, blue sphere on the left and blue glowing dots scattered throughout.

To help reduce the likelihood of losing future cultivated crops to drought and other seasonal hardships, researchers from ORNL, Budapest and Hungary are using neutrons, light microscopy and transmission electron microscopy to study the 'Never Never' plant, known for its ability to endure periods of little to no rain. 

Illustration of a quantum experiment: atoms in a lattice (inset) with entanglement effects radiating from a central particle on a textured surface.

Working at nanoscale dimensions, billionths of a meter in size, a team of scientists led by ORNL revealed a new way to measure high-speed fluctuations in magnetic materials. Knowledge obtained by these new measurements could be used to advance technologies ranging from traditional computing to the emerging field of quantum computing. 

Photo is a graphical representation of lithium ions (glowing orbs) move through a diffusion gate (gold triangle) in a solid-state electrolyte

A team of scientists led by a professor from Duke University discovered a way to help make batteries safer, charge faster and last longer. They relied on neutrons at ORNL to understand at the atomic scale how lithium moves in lithium phosphorus sulfur chloride, a promising new type of solid-state battery material known as a superionic compound. 

quantum network illustration

Researchers at ORNL joined forces with EPB of Chattanooga and the University of Tennessee at Chattanooga to demonstrate the first transmission of an entangled quantum signal using multiple wavelength channels and automatic polarization stabilization over a commercial network with no downtime.

A graphic representation of AI

The Department of Energy announced a $67 million investment in several AI projects from institutions in both government and academia as part of its AI for Science initiative. Six ORNL-led (or co-led) projects received funding.

FAMU, FSU, FAMU-FSU College of Engineering, and Oak Ridge National Laboratory (ORNL) leadership

Oak Ridge National Laboratory has launched its Neutron Nexus pilot program with Florida Agricultural & Mechanical University and Florida State University through the FAMU-FSU College of Engineering. The first program of its kind nationwide, it’s aimed at broadening and diversifying the scientific user community with outreach to universities and colleges. 

Illustration of an electron beam ejecting a carbon atom from graphene

A new technology to continuously place individual atoms exactly where they are needed could lead to new materials for devices that address critical needs for the field of quantum computing and communication that cannot be produced by conventional means.

This illustration demonstrates how atomic configurations with an equiatomic concentration of niobium (Nb), tantalum (Ta) and vanadium (V) can become disordered. The AI model helps researchers identify potential atomic configurations that can be used as shielding for housing fusion applications in a nuclear reactor. Credit: Massimiliano Lupo Pasini/ORNL, U.S. Dept. of Energy

A study led by the Department of Energy’s Oak Ridge National Laboratory details how artificial intelligence researchers created an AI model to help identify new alloys used as shielding for housing fusion applications components in a nuclear reactor. The findings mark a major step towards improving nuclear fusion facilities.