Skip to main content
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.

Picture2.png

Oak Ridge National Laboratory scientists studying fuel cells as a potential alternative to internal combustion engines used sophisticated electron microscopy to investigate the benefits of replacing high-cost platinum with a lower cost, carbon-nitrogen-manganese-based catalyst.

As hurricanes formed in the Gulf Coast, ORNL activated a computing technique to quickly gather building structure data from Texas’ coastal counties. Credit: Mark Tuttle/Oak Ridge National Laboratory, U.S. Dept. of Energy

Geospatial scientists at Oak Ridge National Laboratory have developed a novel method to quickly gather building structure datasets that support emergency response teams assessing properties damaged by Hurricanes Harvey and Irma. By coupling deep learning with high-performance comp...

ORNL’s Frank Combs and Michael Starr of the U.S. Armed Forces (driver) work in ORNL’s Vehicle Security Laboratory to evaluate a prototype device that can detect network intrusions in all modern vehicles. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

A new Oak Ridge National Laboratory-developed method promises to protect connected and autonomous vehicles from possible network intrusion. Researchers built a prototype plug-in device designed to alert drivers of vehicle cyberattacks. The prototype is coded to learn regular timing...

ORNL’s Xiahan Sang unambiguously resolved the atomic structure of MXene, a 2D material promising for energy storage, catalysis and electronic conductivity. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Carlos Jones

Researchers have long sought electrically conductive materials for economical energy-storage devices. Two-dimensional (2D) ceramics called MXenes are contenders. Unlike most 2D ceramics, MXenes have inherently good conductivity because they are molecular sheets made from the carbides ...

Advanced materials take flight in the LEAP engine, featuring ceramic matrix composites developed over a quarter-century by GE with help from DOE and ORNL. Image credit: General Electric

Ceramic matrix composite (CMC) materials are made of coated ceramic fibers surrounded by a ceramic matrix. They are tough, lightweight and capable of withstanding temperatures 300–400 degrees F hotter than metal alloys can endure. If certain components were made with CMCs instead o...