Filter News
Area of Research
News Topics
- (-) Bioenergy (13)
- (-) Fusion (4)
- (-) Grid (2)
- (-) Machine Learning (4)
- (-) Materials Science (4)
- (-) Mercury (1)
- (-) Nanotechnology (3)
- (-) Net Zero (2)
- (-) Quantum Science (3)
- (-) Security (2)
- 3-D Printing/Advanced Manufacturing (2)
- Artificial Intelligence (4)
- Big Data (4)
- Biology (16)
- Biomedical (3)
- Biotechnology (2)
- Buildings (4)
- Chemical Sciences (4)
- Clean Water (2)
- Climate Change (21)
- Composites (1)
- Computer Science (9)
- Coronavirus (3)
- Cybersecurity (3)
- Decarbonization (14)
- Energy Storage (2)
- Environment (25)
- Exascale Computing (3)
- Frontier (4)
- High-Performance Computing (7)
- Hydropower (3)
- Isotopes (1)
- ITER (1)
- Materials (8)
- Microscopy (7)
- National Security (7)
- Neutron Science (3)
- Nuclear Energy (3)
- Partnerships (1)
- Physics (2)
- Polymers (1)
- Quantum Computing (5)
- Simulation (3)
- Summit (4)
- Sustainable Energy (13)
- Transportation (3)
Media Contacts
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