Filter News
Area of Research
- (-) Neutron Science (11)
- (-) Supercomputing (29)
- Advanced Manufacturing (1)
- Biology and Environment (11)
- Clean Energy (30)
- Computer Science (1)
- Fusion Energy (1)
- Isotopes (1)
- Materials (18)
- Materials for Computing (3)
- National Security (4)
- Nuclear Science and Technology (3)
- Quantum information Science (1)
News Topics
- (-) Artificial Intelligence (6)
- (-) Big Data (8)
- (-) Bioenergy (5)
- (-) Biomedical (12)
- (-) Grid (2)
- (-) Polymers (3)
- (-) Summit (15)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (1)
- Biology (1)
- Chemical Sciences (1)
- Climate Change (1)
- Composites (1)
- Computer Science (34)
- Coronavirus (10)
- Critical Materials (1)
- Cybersecurity (1)
- Decarbonization (1)
- Energy Storage (1)
- Environment (5)
- Exascale Computing (2)
- Frontier (1)
- Fusion (1)
- High-Performance Computing (2)
- Isotopes (1)
- Machine Learning (4)
- Materials (2)
- Materials Science (12)
- Mathematics (1)
- Microscopy (2)
- Molten Salt (1)
- Nanotechnology (9)
- National Security (1)
- Neutron Science (30)
- Nuclear Energy (2)
- Physics (6)
- Quantum Science (11)
- Security (1)
- Sustainable Energy (4)
- Transportation (4)
Media Contacts
The US Department of Energy’s Oak Ridge National Laboratory is once again officially home to the fastest supercomputer in the world, according to the TOP500 List, a semiannual ranking of the world’s fastest computing systems.
The U.S. Department of Energy’s Oak Ridge National Laboratory today unveiled Summit as the world’s most powerful and smartest scientific supercomputer.
Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the