Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Filter News
Area of Research
News Topics
- (-) Artificial Intelligence (45)
- (-) Mercury (7)
- 3-D Printing/Advanced Manufacturing (36)
- Advanced Reactors (8)
- Big Data (21)
- Bioenergy (49)
- Biology (57)
- Biomedical (28)
- Biotechnology (10)
- Buildings (17)
- Chemical Sciences (21)
- Clean Water (14)
- Climate Change (47)
- Composites (6)
- Computer Science (81)
- Coronavirus (17)
- Critical Materials (1)
- Cybersecurity (14)
- Decarbonization (43)
- Education (1)
- Emergency (2)
- Energy Storage (28)
- Environment (100)
- Exascale Computing (24)
- Fossil Energy (4)
- Frontier (23)
- Fusion (29)
- Grid (23)
- High-Performance Computing (42)
- Hydropower (5)
- Isotopes (26)
- ITER (2)
- Machine Learning (21)
- Materials (40)
- Materials Science (43)
- Mathematics (5)
- Microelectronics (2)
- Microscopy (20)
- Molten Salt (1)
- Nanotechnology (16)
- National Security (34)
- Net Zero (8)
- Neutron Science (47)
- Nuclear Energy (52)
- Partnerships (15)
- Physics (28)
- Polymers (8)
- Quantum Computing (20)
- Quantum Science (30)
- Renewable Energy (1)
- Security (10)
- Simulation (30)
- Software (1)
- Space Exploration (12)
- Summit (30)
- Sustainable Energy (43)
- Transformational Challenge Reactor (3)
- Transportation (27)
Media Contacts
Sometimes solutions to the biggest problems can be found in the smallest details. The work of biochemist Alex Johs at Oak Ridge National Laboratory bears this out, as he focuses on understanding protein structures and molecular interactions to resolve complex global problems like the spread of mercury pollution in waterways and the food supply.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.