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Media Contacts
Currently, the biggest hurdle for electric vehicles, or EVs, is the development of advanced battery technology to extend driving range, safety and reliability.
ORNL’s Fulvia Pilat and Karren More recently participated in the inaugural 2023 Nanotechnology Infrastructure Leaders Summit and Workshop at the White House.
The Exascale Small Modular Reactor effort, or ExaSMR, is a software stack developed over seven years under the Department of Energy’s Exascale Computing Project to produce the highest-resolution simulations of nuclear reactor systems to date. Now, ExaSMR has been nominated for a 2023 Gordon Bell Prize by the Association for Computing Machinery and is one of six finalists for the annual award, which honors outstanding achievements in high-performance computing from a variety of scientific domains.
The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.
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.
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.
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
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.