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The Department of Energy’s Office of Science has allocated supercomputer access to a record-breaking 75 computational science projects for 2024 through its Innovative and Novel Computational Impact on Theory and Experiment, or INCITE, program. DOE is awarding 60% of the available time on the leadership-class supercomputers at DOE’s Argonne and Oak Ridge National Laboratories to accelerate discovery and innovation.
Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.
As current courses through a battery, its materials erode over time. Mechanical influences such as stress and strain affect this trajectory, although their impacts on battery efficacy and longevity are not fully understood.
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.
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
ORNL’s Debangshu Mukherjee has been named an npj Computational Materials “Reviewer of the Year.”
Lawrence Livermore National Laboratory’s Lori Diachin will take over as director of the Department of Energy’s Exascale Computing Project on June 1, guiding the successful, multi-institutional high-performance computing effort through its final stages.
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.
With the world’s first exascale supercomputing system now open to full user operations, research teams are harnessing Frontier’s power and speed to tackle some of the most challenging problems in modern science.
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.