Skip to main content
U.S. Department of Energy and Cray to Deliver Record-Setting Frontier Supercomputer at ORNL

OAK RIDGE, Tenn., May 7, 2019—The U.S. Department of Energy today announced a contract with Cray Inc. to build the Frontier supercomputer at Oak Ridge National Laboratory, which is anticipated to debut in 2021 as the world’s most powerful computer with a performance of greater than 1.5 exaflops.

Small modular reactor computer simulation

In a step toward advancing small modular nuclear reactor designs, scientists at Oak Ridge National Laboratory have run reactor simulations on ORNL supercomputer Summit with greater-than-expected computational efficiency.

The illustrations show how the correlation between lattice distortion and proton binding energy in a material affects proton conduction in different environments. Mitigating this interaction could help researchers improve the ionic conductivity of solid materials.

Ionic conduction involves the movement of ions from one location to another inside a material. The ions travel through point defects, which are irregularities in the otherwise consistent arrangement of atoms known as the crystal lattice. This sometimes sluggish process can limit the performance and efficiency of fuel cells, batteries, and other energy storage technologies.

Transportation Energy Data Book Edition 37

Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.

In this MXene electrode, choosing the appropriate solvent for the electrolyte can increase energy density significantly. This scanning electron microscopy image shows fine features of a film only 5 microns thick—approximately 10 times narrower than a human hair. Credit: Drexel University; image by Tyler Mathis
Scientists at ORNL, Drexel University and their partners have discovered a way to improve the energy density of promising energy-storage materials, conductive two-dimensional ceramics called MXenes.
ORNL will use state-of-the-art R&D tools at the Battery Manufacturing Facility to develop new methods for separating and reclaiming valuable materials from spent EV batteries.

The use of lithium-ion batteries has surged in recent years, starting with electronics and expanding into many applications, including the growing electric and hybrid vehicle industry. But the technologies to optimize recycling of these batteries have not kept pace.

Laminations such as these are compiled to form the core of modern electric vehicle motors. ORNL has developed a software toolkit to speed the development of new motor designs and to improve the accuracy of their real-world performance.

Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.

The EPB Control Center monitors the company’s assets such as substations and buildings.

OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).

Researchers analyzed the oxygen structure (highlighted in red) found in a perovskite’s crystal structure at room temperature, 500°C and 900°C using neutron scattering at ORNL’s Spallation Neutron Source. Analyzing how these structures impact solid oxide f

A University of South Carolina research team is investigating the oxygen reduction performance of energy conversion materials called perovskites by using neutron diffraction at Oak Ridge National Laboratory’s Spallation Neutron Source.

ORNL scientists used commuting behavior data from East Tennessee to demonstrate how machine learning models can easily accept new data, quickly re-train themselves and update predictions about commuting patterns. Credit: April Morton/Oak Ridge National La

Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.