A new nanoscience study led by a researcher at ORNL takes a big-picture look at how scientists study materials at the smallest scales.
An advance in a topological insulator material — whose interior behaves like an electrical insulator but whose surface behaves like a conductor — could revolutionize the fields of next-generation electronics and quantum computing, according to scientists at ORNL.
Two years after ORNL provided a model of nearly every building in America, commercial partners are using the tool for tasks ranging from designing energy-efficient buildings and cities to linking energy efficiency to real estate value and risk.
When Hurricane Maria battered Puerto Rico in 2017, winds snapped trees and destroyed homes, while heavy rains transformed streets into rivers. But after the storm passed, the human toll continued to grow as residents struggled without electricity for months. Five years later, power outages remain long and frequent.
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.
University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.
A study led by researchers at ORNL used the nation’s fastest supercomputer to close in on the answer to a central question of modern physics that could help conduct development of the next generation of energy technologies.
The U.S. Department of Energy’s Office of Science announced allocations of supercomputer access to 51 high-impact computational science projects for 2022 through its Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
The U.S. Department of Energy’s Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program is seeking proposals for high-impact, computationally intensive research campaigns in a broad array of science, engineering and computer science domains.