Filter News
Area of Research
- (-) Supercomputing (21)
- Biology and Environment (23)
- Biology and Soft Matter (1)
- Clean Energy (23)
- Computer Science (1)
- Electricity and Smart Grid (1)
- Functional Materials for Energy (2)
- Fusion and Fission (3)
- Isotopes (3)
- Materials (21)
- Materials for Computing (3)
- National Security (13)
- Neutron Science (8)
News Topics
- (-) Artificial Intelligence (7)
- (-) Climate Change (4)
- (-) Cybersecurity (1)
- (-) Energy Storage (3)
- (-) Frontier (7)
- (-) Quantum Science (4)
- (-) Space Exploration (1)
- Big Data (3)
- Bioenergy (2)
- Biology (5)
- Biomedical (4)
- Buildings (3)
- Chemical Sciences (2)
- Computer Science (10)
- Coronavirus (3)
- Critical Materials (1)
- Decarbonization (2)
- Environment (3)
- Exascale Computing (6)
- Grid (2)
- High-Performance Computing (8)
- Machine Learning (5)
- Materials (8)
- Materials Science (5)
- Microscopy (2)
- Nanotechnology (3)
- National Security (3)
- Neutron Science (1)
- Partnerships (1)
- Physics (1)
- Quantum Computing (7)
- Security (2)
- Simulation (5)
- Summit (7)
- Sustainable Energy (2)
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.