First-generation graduate Brittany Rodriguez advances manufacturing scie...
Two ORNL teams recently completed Cohort 18 of Energy I-Corps, an immersive two-month training program where the scientists define their technology’s value propositions, conduct stakeholder discovery interviews and develop viable market pathways.
Power companies and electric grid developers turn to simulation tools as they attempt to understand how modern equipment will be affected by rapidly unfolding events in a complex grid.
Researchers conduct largest, most accurate molecular dynamics simulations to date of two million correlated electrons using Frontier, the world’s fastest supercomputer. The simulation, which exceed an exaflop using full double precision, is 1,000 times greater in size and speed than any quantum chemistry simulation of it's kind.
In the wet, muddy places where America’s rivers and lands meet the sea, scientists from the Department of Energy’s Oak Ridge National Laboratory are unearthing clues to better understand how these vital landscapes are evolving under climate change.
Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.
ORNL researchers completed successful testing of a gallium nitride transistor for use in more accurate sensors operating near the core of a nuclear reactor. This is an important technical advance particularly for monitoring new, compact.
Researchers used quantum simulations to obtain new insights into the nature of neutrinos — the mysterious subatomic particles that abound throughout the universe — and their role in the deaths of massive stars.
Anuj J. Kapadia, who leads the Advanced Computing in Health Sciences Section at the Department of Energy’s Oak Ridge National Laboratory, was named a 2024 Fellow by the American Association of Physicists in Medicine.
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
A team led by researchers at ORNL explored training strategies for one of the largest artificial intelligence models to date with help from the world’s fastest supercomputer. The findings could help guide training for a new generation of AI models for scientific research.