Filter News
Area of Research
- (-) Materials (18)
- (-) Supercomputing (20)
- Advanced Manufacturing (1)
- Biology and Environment (14)
- Clean Energy (22)
- Computer Science (1)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (1)
- Fusion and Fission (5)
- Isotopes (1)
- Materials for Computing (4)
- National Security (13)
- Neutron Science (2)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (5)
- (-) Computer Science (11)
- (-) Frontier (7)
- (-) Grid (3)
- (-) Machine Learning (5)
- (-) Materials Science (11)
- Advanced Reactors (1)
- Artificial Intelligence (9)
- Big Data (3)
- Bioenergy (5)
- Biology (7)
- Biomedical (5)
- Buildings (4)
- Chemical Sciences (11)
- Clean Water (1)
- Climate Change (4)
- Composites (2)
- Coronavirus (5)
- Critical Materials (4)
- Cybersecurity (1)
- Decarbonization (4)
- Energy Storage (14)
- Environment (6)
- Exascale Computing (6)
- Fusion (1)
- High-Performance Computing (10)
- Isotopes (2)
- Materials (28)
- Microscopy (5)
- Nanotechnology (7)
- National Security (3)
- Neutron Science (5)
- Nuclear Energy (1)
- Partnerships (4)
- Physics (6)
- Polymers (3)
- Quantum Computing (7)
- Quantum Science (6)
- Security (2)
- Simulation (5)
- Space Exploration (1)
- Summit (7)
- Sustainable Energy (3)
- Transformational Challenge Reactor (1)
- Transportation (2)
Media Contacts
A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory 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.
Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant