Hosted by the Quantum Computing Institute and the Oak Ridge Leadership Computing Facility, the fourth annual event brought together over 100 attendees to discuss the latest developments in quantum computing and to learn about results from projects supported by the OLCF’s Quantum Computing User Program.
Wildfires have shaped the environment for millennia, but they are increasing in frequency, range and intensity in response to a hotter climate. The phenomenon is being incorporated into high-resolution simulations of the Earth’s climate by scientists at the Department of Energy’s Oak Ridge National Laboratory, with a mission to better understand and predict environmental change.
To support the development of a revolutionary new open fan engine architecture for the future of flight, GE Aerospace has run simulations using the world’s fastest supercomputer capable of crunching data in excess of exascale speed, or more than a quintillion calculations per second.
For the third year in a row, the Quantum Science Center held its signature workforce development event: a comprehensive summer school for students and early-career scientists designed to facilitate conversations and hands-on activities related to
ORNL’s Debangshu Mukherjee has been named an npj Computational Materials “Reviewer of the Year.”
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
The Oak Ridge Leadership Computing Facility’s Matt Sieger has been named the project director for the OLCF-6 effort. This next OLCF undertaking will plan and build a world-class successor to the OLCF’s still-new exascale system, Frontier.
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.