Skip to main content
Distinguished Inventors

Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.

Simulation of short polymer chains

Oak Ridge National Laboratory scientists have discovered a cost-effective way to significantly improve the mechanical performance of common polymer nanocomposite materials.

Computing – Mining for COVID-19 connections

Scientists have tapped the immense power of the Summit supercomputer at Oak Ridge National Laboratory to comb through millions of medical journal articles to identify potential vaccines, drugs and effective measures that could suppress or stop the

Before the demonstration, the team prepared QKD equipment (pictured) at ORNL. Image credit: Genevieve Martin/Oak Ridge National Laboratory, U.S. Dept. of Energy

For the second year in a row, a team from the Department of Energy’s Oak Ridge and Los Alamos national laboratories led a demonstration hosted by EPB, a community-based utility and telecommunications company serving Chattanooga, Tennessee.

The agreement builds upon years of collaboration, including a 2016 effort using modeling tools developed at ORNL to predict the first six months of operations of TVA’s Watts Bar Unit 2 nuclear power plant. Credit: Andrew Godfrey/Oak Ridge National Laboratory, U.S. Dept. of Energy

OAK RIDGE, Tenn., Feb. 19, 2020 — The U.S. Department of Energy’s Oak Ridge National Laboratory and the Tennessee Valley Authority have signed a memorandum of understanding to evaluate a new generation of flexible, cost-effective advanced nuclear reactors.

Materials—Polymer-theory-problem

Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the