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1 - 10 of 15 Results

The National Center for Computational Sciences, located at the Department of Energy’s 91°µÍø, made a strong showing at computing conferences this fall. Staff from across the center participated in numerous workshops and invited speaking engagements.

The Department of Energy’s 91°µÍø announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.

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 91°µÍø, with a mission to better understand and predict environmental change.

A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.

Researchers at the Department of Energy’s 91°µÍø and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.

The Frontier supercomputer at the Department of Energy’s 91°µÍø earned the top ranking today as the world’s fastest on the 59th TOP500 list, with 1.1 exaflops of performance. The system is the first to achieve an unprecedented level of computing performance known as exascale, a threshold of a quintillion calculations per second.

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.

A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.

A team of scientists led by the Department of Energy’s 91°µÍø and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.